Data Engineer (AI Platform) hiring kit

Every step to hire the data engineer whose pipelines feed your models and retrieval indexes — and who can show the data is fresh, unique and allowed to be there.

About this kit — the 5Ws

A data engineer on an AI platform builds and runs the pipelines that models and retrieval systems depend on: preparing training data, loading documents into a search index, versioning each dataset so a result can be reproduced, and checking quality before anything downstream reads it. The hard part is rarely moving data from A to B. It is making a pipeline safe to rerun, tracing a bad record back to the source change that caused it, keeping personal data out of places it should not reach, and deciding how fresh the data really needs to be for what it costs.

Calibrated for:: Mid-level through senior individual contributor who has already run a pipeline in production; not calibrated for a first data hire who must also set up the whole warehouse from nothing.
Timeline:: Set an approval-to-offer target at intake, allowing for panel availability, candidate adjustments, references and any lawful checks.

Who: Engineering and data leaders, and the recruiters working with them, hiring someone to own the data behind model training, fine-tuning or retrieval — whether or not the company already has a general data team.
What: This kit gives you a weighted competency model, a pipeline-debugging work sample, structured technical questions with strong and weak answers, a job description and a 30-60-90 plan, plus a way to look back at how the hiring process itself ran.
When: Pick it up when models or a retrieval feature start depending on data nobody formally owns — ideally before the requisition is approved — and keep using it until you have logged how long each step of the hire took.
Where: Technology teams training models, fine-tuning them, or serving answers from a retrieval index.
Why: Data failures in AI systems tend to be quiet: a duplicated record or a stale document can produce a wrong answer without triggering any pipeline error. A shared rubric and a realistic broken pipeline let you compare how each candidate finds that kind of fault, instead of comparing lists of tools.

The competency model · 6

Use these competencies to compare job-related evidence. Review any apparent gap in a must-have before deciding.

Pipeline reliability and rerunsMust-have · 20
Builds jobs that give the same result when run twice (idempotent), can backfill a date range or replay a source without creating duplicates, and fails loudly instead of loading half a batch.
Data-quality checksMust-have · 20
Writes checks for uniqueness, nulls, freshness and row counts against the source, decides which failures block a load and which only warn, and can name the check that would have caught a past incident.
Lineage and dataset versioningMust-have · 15
Can trace a record in a training set or index back to its source and the job that produced it, and versions datasets so a model result or an answer can be reproduced later.
Data modelling and SQLMust-have · 15
Writes clear SQL with careful joins, is precise about what one row means (the grain), and designs merge keys and schemas that survive a change in the upstream system.
Access control and personal dataMust-have · 15
Limits who and what can read each dataset, keeps personal or restricted fields out of training sets and indexes unless there is an approved reason, and can find every place a person's data was copied when a deletion request arrives.
Cost and freshness trade-offsCan learn · 15
Knows what a full reload, an incremental load and a re-embedding run each cost, agrees a freshness target with the people who use the data, and explains the trade-off in plain language.

What's required and what's optional

Your hiring journey for Data Engineer (AI Platform). Select a stage to jump to it.

Required for every hire
Optional — use when it adds evidence
AI boost — a big drafting win
Colour = phase

Phase 1
Plan
1. 01Plan the roleRequired
2. 02Agree successRequiredAI boost
Phase 2
Attract
1. 03Write the jobRequiredAI boost
2. 04Find candidatesOptionalAI boost
Phase 3
Select
Phase 4
Decide
Phase 5
Onboard & learn
1. 12Help them settle inRequired
2. 13Measure time to hireRequiredAI boost

AI drafts, people decide: Use AI to draft, rewrite and summarise your own material — never to rank, score, screen out or decide on candidates, and never with a candidate's personal data.

Plan

Attract

Select

Decide

Onboard & learn

Solid = required · dashed = optional · arrow keys move between steps

Stage 1 of 13Required

0 of 3 done

Plan the role

Workforce planning and requisition approval

Who runs it: Hiring manager + finance / HR business partner
Done when: An approved requisition with a written business case, budget and a named decision-maker.

Key tool

Answer these before the role is approved

1Which training sets, fine-tuning sets or retrieval indexes will this person own in their first year?
2When did a model or assistant last give a wrong answer because of the data it read, and how long did it take to find out?
3Could someone in the current data or backend team take this on within two quarters?

AI as an advantage4D framework

Delegate: Hand AI the first draft of the business case and the build / borrow / buy comparison. The decision, the budget and the sign-off stay with people.

Show the prompt and the checks

Describe: You are helping a hiring manager write a business case for a Data Engineer (AI Platform) role. Context: I will paste the problem the hire solves, the work it involves, the capacity we already have and the time we have. Produce: (1) a one-sentence problem statement in business terms; (2) a build / borrow / buy comparison with the strongest argument for each; (3) the questions a finance partner would ask; and (4) a short recommendation with the evidence it rests on. Use British English and plain language. Do not invent figures — leave [brackets] where I need to supply numbers. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check every number and claim against your own notes and strike anything the draft assumed. Make sure 'buy' is argued from ongoing work, not from a job title.
Diligence: The hiring manager owns the final case and the budget request. No candidate information belongs here; AI drafts the case, people decide whether to open the role.

Don't miss: Expecting a general data engineer to also train models. If the open question is 'is the model good enough?', look at a machine learning engineer; if it is 'can we trust what the model is reading?', this is the role.

Framework

Build · Borrow · Buy

Before opening a role, decide whether to build the capability in your current team, borrow it (contractor, agency, partner), or buy it (a permanent hire).

Write the problem the hire solves in one sentence, in business terms — not the job title.
Test Build: could an existing person grow into this within the time you have?
Test Borrow: is the need time-boxed, or does it end once a system is shipped?
Choose Buy only when the work is ongoing, core to the product and nobody inside can absorb it.
Record the decision, the budget and the one person who signs off the final hire.

Why it works: Writing the build, borrow or buy decision down exposes a vague or temporary need before it becomes a permanent requisition — and gives every later stage an anchor to check against.

• Name who makes the final call — usually the leader who owns the AI platform or the data platform the hire will join.

Is this a dedicated role?

Decide whether the work is sustained and substantial enough for a dedicated owner by writing down the workload, the risk, the capacity you already have and the outcomes you expect.

Answer these before the role is approved

1.Which training sets, fine-tuning sets or retrieval indexes will this person own in their first year?
Listen for: Specific training sets or indexes that an AI feature already reads, or one with a funded launch date.
Red flag: 'We need better data for AI' with no dataset or feature named.
2.When did a model or assistant last give a wrong answer because of the data it read, and how long did it take to find out?
Listen for: A specific incident, or an honest 'we would not know' — either one is the case for the hire.
3.Could someone in the current data or backend team take this on within two quarters?
Listen for: A named person with a realistic plan to learn the AI-specific parts, or a clear reason nobody has capacity.
4.Is this ongoing work, or a single migration that ends?
Listen for: Several sources and AI features that keep changing point to a permanent hire; one clean-up of one index may suit a contractor.
5.What does each month without an owner cost — in wrong answers, rework or warehouse spend?
Listen for: A concrete consequence such as support tickets, a delayed model, or backfills nobody is watching.

Build, borrow or buy?

Build: move an analytics or backend engineer onto the AI data pipelines with protected learning time and a mentor who has run a production index.
Borrow: bring in a contractor to add checks and versioning to the riskiest pipeline, with a fixed date for handing it to a named owner.
Buy: hire permanently once more than one AI feature depends on pipelines that change upstream and need continuous care.

Watch out for

Opening the role before anyone can say which source systems feed your AI features, so the new hire spends the first quarter just drawing the map.

Stage 2 of 13Required

0 of 2 done

Agree success

Intake meeting and competency model

Who runs it: Recruiter + hiring manager
Done when: A signed-off competency model: 5–7 competencies, each marked must-have or trainable, with weights.

Key tool

Ask the hiring manager

1Which of the systems or areas this person will own worries you most, and why?
2When something there went wrong recently, how did you find out?
3In 12 months, what will be shipped, fixed or running for you to call this hire a success? Agree two or three measurable outcomes.
4Who will they work with most closely?
5What could a strong candidate learn in their first three months? Those capabilities are 'trainable' — never use them to filter applications.

AI as an advantageAI boost4D framework

Delegate: Hand AI your raw intake notes to turn into a draft competency model. Choosing the must-haves and the weights stays with the hiring manager and recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter and a hiring manager build a competency model for a Data Engineer (AI Platform) role. Context: I will paste the notes from our intake meeting, including the 12-month outcomes and what the hiring manager worries about most. Produce a table of 5–7 observable competencies with, for each: a one-line definition; whether it is a must-have or trainable, with the reason; what evidence of it looks like in real work; and a suggested weight so the weights total 100. Use British English and inclusive wording, avoid years-of-experience or named employers as evidence, and keep every competency job-related. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that each competency is observable and job-related, that nothing describes a type of person rather than the work, and that trainable items haven't been marked must-have.
Diligence: The hiring manager signs off the final model and weights before anyone meets a candidate. No candidate data goes into the prompt; AI drafts, people decide.

Don't miss: Making one orchestration or warehouse product a must-have. Engineers move between these tools quickly; idempotency and lineage habits take far longer to build.

Framework

Competency model (must-have vs trainable)

A short list of the observable capabilities the job needs, split into what the hire must bring on day one and what you will teach.

Start from the 12-month outcomes, then ask which capabilities produce them.
Keep 5–7 competencies. With more, interviewers struggle to score each one separately.
Mark each must-have or trainable. Only must-haves can count against a candidate.
Give each a weight so they sum to 100, agreed before anyone meets a candidate.
Write what evidence of each looks like, so interviewers know what they are listening for.

Why it works: Agreeing the criteria before meeting candidates stops the bar moving from one candidate to the next, and makes it harder for 'I'll know it when I see it' to decide the hire.

Core intake questions (ask for every role)

Book 45 minutes with the hiring manager before anything is advertised, and write the answers down.
Which of the systems or areas this person will own worries you most, and why?
When something there went wrong recently, how did you find out?
In 12 months, what will be shipped, fixed or running for you to call this hire a success? Agree two or three measurable outcomes.
Who will they work with most closely?
What could a strong candidate learn in their first three months? Those capabilities are 'trainable' — never use them to filter applications.
Who is on the interview panel, and is their time held for the next four to six weeks?

Questions specific to this role

1.Which source systems feed the AI datasets, and who tells us when one of them changes its export?
Listen for: A list of sources and a named contact or process. If the answer is 'we find out when it breaks', weight data-quality checks higher.
2.Do any of these datasets contain personal or confidential data, and who approves what may go into a training set or index?
Listen for: A clear owner for approval. It decides how much weight access control and personal data should carry.
3.How fresh does each dataset need to be, and has anyone priced the difference between hourly and nightly?
Listen for: Targets tied to a user need, or an admission that nobody has set one — which becomes early work for the hire.

What success looks like in 12 months

Every dataset an AI feature reads has a named owner, a freshness target and at least one blocking quality check.
Any record in the main retrieval index or training set can be traced to its source and the job run that loaded it.
A backfill or replay can run without creating duplicates, and the team has done one for real.
For a deletion request, the team can list where that person's data was copied, including embeddings, apply the agreed deletion and retention policy to each copy, record when it was done, and escalate any open question about trained models.

Watch out for

Treating 'has built a vector database' as the core requirement, when most of the job is upstream: getting clean, current, permitted records to the index at all.

Stage 3 of 13Required

0 of 3 done

Write the job

Job description and pay band

Who runs it: Hiring manager (content) + recruiter (publishing)
Done when: A published job description that states outcomes, the must-haves, how AI tools are used and the pay information the law requires.

Key tool

Data Engineer (AI Platform)

We're hiring a data engineer to own the pipelines behind [product]'s AI features. You'll prepare training and retrieval data, version it so results can be reproduced, build the checks that stop bad records reaching a model or index, and decide with [team] how fresh each dataset needs to be for what it costs.

What you'll do

•Build and run pipelines that load source data into training sets, fine-tuning sets and retrieval indexes.
•Make every job safe to rerun, backfill or replay without duplicating or losing records.
•Write data-quality checks for uniqueness, freshness, nulls and counts, and decide which ones block a load.
•Version datasets and record lineage so any record can be traced to its source and loading job.
•Control who can read each dataset and keep personal or restricted fields out unless their use is approved.
•Track the cost of loads and embedding runs and agree freshness targets with the people who use the data.

What you need

•You have built and run at least one production data pipeline, including after the source changed underneath it.
•You write clear, tested SQL and [Python/other] for transforms that run unattended.
•You can show a data-quality check you wrote and an incident it caught or would have caught.
•You have handled restricted or personal data and can explain how you limited access to it.

Nice to have

•Experience loading documents into a search or vector index, including chunking and re-embedding.
•Experience preparing or versioning training data for a machine learning team.
•Experience with change-data capture or streaming sources.
•Familiarity with data-protection requirements that apply to training data, such as deletion requests.

How we use AI tools

•Using AI tools to draft SQL, transforms or tests is welcome where company policy allows; you'll be expected to read and run what they produce before trusting it.
•You'll be able to tell when a model-based step, such as AI-assisted deduplication, adds risk a deterministic rule would avoid.
•Any AI tools you lean on during the work sample are fine — list them in your submission with a line on how you checked their output.

Pay

Salary range: [currency, minimum–maximum], plus [equity/bonus/benefits]. The range reflects [location/working pattern]. We place every offer within it using the same pre-set, job-related, gender-neutral factors — level and the number of pipelines they would own, experience running production pipelines through upstream changes, experience handling restricted or personal data, experience with retrieval indexes or training-data preparation — never previous salary or negotiating style.

AI as an advantageAI boost4D framework

Delegate: Hand AI the first draft of the job description from your signed-off competency model. The must-haves, the pay range and the final wording stay with people.

Show the prompt and the checks

Describe: You are helping write an outcome-based job description for a Data Engineer (AI Platform) role. Context: I will paste the competency model (must-have and trainable, with evidence), the 12-month outcomes, how the team uses AI tools, the location and working pattern, and the pay range. Produce: a summary paragraph that leads with the problem and what the hire will own; 'What you'll do' as outcomes, not tasks; 'What you need' containing only the must-haves; 'Nice to have' for the trainable items; 'How we use AI tools'; and a pay section that states the range. Use British English and inclusive, gender-neutral wording, avoid hype, and replace any 'X years' with the capability it stands for. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that 'What you need' contains only the must-haves, that no degree, employer or years requirement crept in, and that the pay range and location are exactly what was approved.
Diligence: The hiring manager owns the content and the recruiter owns publishing, including the pay information the law requires for each location. AI drafts the advert; people decide what it says.

Don't miss: Copying a general analytics data-engineer advert. It undersells the AI-specific parts — embedding runs, dataset versions for training, deletion reaching an index — and attracts people who have never touched them.

Stage 4 of 13Optional

0 of 3 done

Find candidates

Sourcing strategy

Who runs it: Recruiter / sourcer
Done when: A shortlist-ready pipeline from at least three channels, with outreach tracked.

Use when:: Inbound applications won't give you enough qualified candidates.
Skip when:: Your shortlist already comes from inbound applications or referrals.

Key tool

Search query

("data engineer" OR "analytics engineer" OR "data platform") AND ("backfill" OR "idempotent" OR "data quality" OR "lineage") AND ("embedding" OR "vector" OR "retrieval" OR "training data" OR "machine learning") AND (SQL OR Python) NOT ("intern" OR "student")

First outreach message

AI as an advantageAI boost4D framework

Delegate: Hand AI the search queries and the first draft of the outreach template. Choosing channels, personalising to a candidate's public work and sending stay with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter source candidates for a Data Engineer (AI Platform) role. Context: I will paste the must-have competencies, the kind of work we want evidence of, the channels we are using and the pay range. Produce: (1) one search query per channel, using OR for alternatives, AND for things that must all appear and NOT to exclude, with a one-line note on what each query looks for; and (2) a first-outreach template of under 120 words that opens with a [placeholder for something the candidate built], states the problem we are hiring to solve and the pay range, and ends with a low-pressure question. Use British English and inclusive wording. Do not refer to any specific person. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the queries search for evidence of the work rather than job titles alone, and that the template claims nothing about the role or the pay you can't back up.
Diligence: The recruiter personalises every message to something the candidate actually built, and sends it. No names or profile data go into the prompt; AI drafts, people decide whom to contact.

Don't miss: Searching only for 'data engineer' titles. Strong candidates also sit under analytics engineer, platform engineer and backend engineer titles.

Framework

Channel mix + evidence-first outreach

Spread sourcing across channels where the work is visible, and open outreach with the problem, not the perks.

Pick channels where candidates show their work, not only where they list job titles.
Write one search query per channel. OR joins alternatives, AND joins things that must all appear, NOT excludes. Keep the version that works best.
Personalise the first line to something the candidate actually built or wrote.
Lead with the problem and the pay range. Save the perks for later.
Track reply rates by channel each week and drop the channel that isn't working.

Why it works: Searching for evidence of the work can surface candidates whose current title differs from the vacancy, or whose CV undersells what they have done.

Sourcing basics

Adapt the search query for each platform — the same words behave differently on each site.
Candidates with adjacent titles may have relevant evidence. Search for the work and outcomes the role needs, not the current title.
Channel activity is a lead, not evidence. Review linked work and examples against the written competencies.

Where to look

Engineering blogs and incident write-ups about data pipelines

A write-up on a broken backfill or a schema change shows how the person diagnosed and fixed a real data fault.

Open-source data-quality, orchestration and transformation projects

Contributions to these tools are code you can read, including the tests around them.

Referrals from your ML engineers and analysts

People who consumed a candidate's data can say whether it arrived correct and on time, and name the pipeline.

Professional networks, searched on pipeline work such as backfills, lineage and data checks instead of a single job title

This work sits under data, analytics, platform and backend titles, so the description matters more than the job title.

Data engineering meet-ups and community forums

A detailed answer on idempotent loads or merge keys is something you can read and check before reaching out.

Boolean search string

("data engineer" OR "analytics engineer" OR "data platform") AND ("backfill" OR "idempotent" OR "data quality" OR "lineage") AND ("embedding" OR "vector" OR "retrieval" OR "training data" OR "machine learning") AND (SQL OR Python) NOT ("intern" OR "student")

First outreach message

Hello [name] — I read [their write-up, talk or repository] on [specific pipeline problem]. At [company], our AI features read from [index or dataset], and when an upstream system changes, stale or duplicated records reach users before we notice. We're hiring a data engineer to own that, at [range]. Would a short call to compare notes on it be useful?

Stage 5 of 13Required

0 of 3 done

Check applications

Application screening

Who runs it: Recruiter
Done when: Every application reviewed against the same written knockouts within five working days.

Key tool

Reject only if one of these is missing

•Can meet the role's work-authorisation requirements by the start date, with sponsorship where the employer offers it. Don't ask for documents at this stage.
•Describes at least one production data pipeline they built or ran, not only a one-off script or course project.
•Shows working SQL plus one general-purpose language used for production transforms.

AI as an advantage4D framework

Delegate: Hand AI the job of turning your written knockouts into a consistent checklist with reason codes. Reviewing each application stays with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter prepare an application-screening checklist for a Data Engineer (AI Platform) role. Context: I will paste the must-have competencies and our written knockout criteria. Produce: (1) each knockout as a yes/no question answerable from an application alone; (2) a short list of reason codes for a 'no'; and (3) a note of any criterion that looks like a proxy — a degree, employer brand or employment gap — rather than a job-related requirement, with a suggested rewrite. Use British English. Do not evaluate any applicant and do not ask for applicant details. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that each question is binary and job-related, and that every flagged proxy has been removed or justified in writing.
Diligence: A person reviews every application against the checklist and records the reason. Never paste CVs or applicant details into an AI tool, and never let a tool screen anyone out — in New York City and the EU that can bring automated-decision rules into play.

Don't miss: Marking a candidate down because their warehouse or orchestrator differs from yours. Ask how they made a job safe to rerun; the tool is secondary.

Framework

Knockout criteria

A small set of yes/no requirements, written in advance, applied to every applicant the same way.

Derive knockouts only from must-have competencies and genuine legal requirements.
Keep them binary and answerable from the application itself.
Apply them in the same order to every application and log the reason for each rejection.
Do not use proxies such as university, employer brand or employment gaps as knockouts.
Anything ambiguous gets a second review or moves forward to the skills stage.

Why it works: Early screening rejects people before richer evidence exists, so its criteria need the most care. Written criteria make decisions reviewable — but each one must also be job-related, applied consistently and checked for unjustified adverse impact.

Screening rules

Do not use degrees, employment gaps or certifications as filters unless they are demonstrably job-related and justified for this role.
Judge the claims in a CV, not how polished it is — a well-written CV may or may not have had AI help, and either way it is the evidence that matters.

Knockout criteria

Can meet the role's work-authorisation requirements by the start date, with sponsorship where the employer offers it. Don't ask for documents at this stage.
Describes at least one production data pipeline they built or ran, not only a one-off script or course project.
Shows working SQL plus one general-purpose language used for production transforms.

Green flags

Mentions making a job safe to rerun, running a backfill or handling a schema change upstream.
Names a data-quality check they wrote and what it caught.
Has loaded data for a model, a feature store or a retrieval index, not only for dashboards.

Red flags

A long list of warehouse, streaming and orchestration products with no pipeline, volume or outcome attached.
Every pipeline described is a nightly full reload, with no sign of handling increments, late data or reruns.
Talks about 'cleaning data' without saying how they knew it was clean afterwards.

Watch out for

Reading 'built an ingestion pipeline' as ownership. Check whether they also ran it once things changed upstream.

Compliance checkpointNew York CityEU

For a New York City employment decision, work out whether a tool is an 'automated employment decision tool' (AEDT) under Local Law 144 — one that uses covered techniques to substantially assist or replace a discretionary decision. Before using a covered tool, make sure an independent bias audit was completed no more than a year earlier, publish the required summary, and give covered candidates or employees the required notice at least ten business days before use. In the EU, the AI Act (Annex III) lists AI used to recruit, filter applications or evaluate candidates as high-risk, subject to the Article 6(3) exception; the related obligations apply from 2 December 2027 under the amended timetable. A manual screen does not exempt an AI tool used elsewhere in the process.

Sources: NYC DCWP — Automated employment decision tools · EU AI Act — consolidated Regulation (EU) 2024/1689 · checked 2026-09-25

Stage 6 of 13Optional

0 of 3 done

Recruiter call

Recruiter screen

Who runs it: Recruiter
Done when: A 20–30 minute call that confirms logistics, motivation and one or two must-have signals.

Use when:: You are hiring at volume, or pay and logistics need confirming early.
Skip when:: You have only a handful of applicants — fold the pay and logistics questions into application screening instead.

Key tool

Ask every candidate, in this order

1Tell me about a pipeline you look after now or recently. Where does the data come from and who uses it?
2What happened the last time one of its sources changed without warning?
3Have any of your pipelines fed a model, a training set or a search index?
4Our range for this role is [range]. Does that work for you?

AI as an advantage4D framework

Delegate: Hand AI the first draft of the screening-call script and its evidence anchors. Running the call and taking the notes stay with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter write a 20–30 minute structured screening call for a Data Engineer (AI Platform) role. Context: I will paste the must-have competencies, the logistics we need to confirm (location, working pattern, notice period) and the pay range. Produce: an opening that explains the process, the timeline and the pay range; 4–6 questions in a fixed order, each with one line on what a strong answer contains; and a closing that asks whether the published range works. Use British English and inclusive wording. Do not include questions about salary history, age, family, health or any other protected characteristic. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that every question is job-related and lawful, that the pay question asks whether the range works rather than for history, and that the anchors describe evidence, not impressions.
Diligence: The recruiter asks the same questions of everyone and writes the notes. No candidate details go into the prompt; AI drafts the script, people run the call and decide.

Don't miss: Asking the recruiter to judge SQL quality or merge logic. This call covers motivation, logistics and whether the candidate can describe their own work plainly.

Framework

Structured screening call

The same short set of questions for every candidate, with notes taken against what a strong answer contains.

Open by explaining the process, timeline and pay range. No surprises later.
Confirm logistics: location, working pattern, notice period and whether the range works for them.
Ask the same 4–6 questions in the same order, and note evidence, not impressions.
Leave time for the candidate's questions. Don't score their questions unless they test a written, job-related competency.
Decide within 48 hours and tell the candidate either way.

Why it works: Using the same questions and written evidence anchors makes answers easier to compare, and a short, predictable call respects the candidate's time.

Screening call basics

Set the decision timetable before sourcing and tell candidates when to expect each update.
Ask every candidate what draws them to this particular role, and listen for interest in the work described rather than the title.
Confirm notice period and whether they are in other processes, so you can plan the timetable.
End every call by asking whether the published pay range works — never ask for salary history.

Recruiter screen questions

1.Tell me about a pipeline you look after now or recently. Where does the data come from and who uses it?
Listen for: Named sources and consumers, and a clear sense of which parts the candidate built.
Red flag: Can describe the tools but not who relies on the data.
2.What happened the last time one of its sources changed without warning?
Listen for: A specific change, how it was noticed and what the candidate did about it.
Red flag: 'That doesn't really happen' or a story where someone else found and fixed it.
3.Have any of your pipelines fed a model, a training set or a search index?
Listen for: A straight answer on how close their experience is to AI data work; dashboards-only experience is not a reject, but note it for the panel.
4.Our range for this role is [range]. Does that work for you?
Listen for: A clear answer on whether the published range works. Don't ask for salary history — this process sets pay from the published band and pre-set, job-related factors.

Compliance checkpointUS (state and city)EU

Don't ask for salary history. In the EU, national laws implementing Article 5(2) of Directive (EU) 2023/970 must prohibit the question. US rules vary, but jurisdictions including California and New York prohibit employers from seeking salary history. Ask whether the published range works instead.

Sources: EU Pay Transparency Directive 2023/970, Article 5 · California Labor Commissioner — salary history · New York State DOL — salary history ban · checked 2026-09-25

Stage 7 of 13Required

0 of 3 done

Work sample

Skills assessment / work sample

Who runs it: Hiring manager (design) + recruiter (logistics)
Done when: Every shortlisted candidate completes the same job-shaped task, marked against criteria written in advance.

Key tool

Find why a retrieval index serves stale and duplicated records

Plan on up to three hours. A nightly pipeline loads a product catalogue into a retrieval index used by a customer assistant. On 3 March the catalogue team switched their export from a full daily snapshot to a feed of changed records, with an 'op' column set to 'upsert' or 'delete'. Since then the assistant has cited discontinued products and returned the same item two or three times. Job log extracts: '01 Mar 02:14 merge_products: source_rows=48,210 target_rows=48,210'; '05 Mar 02:09 merge_products: source_rows=1,932 inserted=1,932 updated=0 deleted=0 target_rows=50,142'; '06 Mar 02:11 embed_chunks: WARN rate limit, retry 2 of 5 succeeded'; '06 Mar 02:12 merge_products: source_rows=2,105 inserted=2,105 updated=0 deleted=0 target_rows=52,247'. Sample rows from the index-ready table (product_id, updated_at, status): 'P-1042, 2026-03-04 18:20, active'; 'P-1042, 2026-03-05 09:02, active'; 'P-0877, 2026-02-27 11:40, active' — while the change note lists P-0877 as deleted on 4 March. The merge step's code matches rows on product_id together with updated_at. Find the real cause, write a corrected merge step, and add one data-quality check that would have stopped the bad load. Decide for yourself whether the embedding retries matter. Your team may also give you the full logs and code as files, but the extracts above are enough to reach a diagnosis.

Planned for up to three hours (adjustments available on request). At the agreed stopping time, write a short note on what you would examine next, and what you deliberately left out.

AI as an advantage4D framework

Delegate: Hand AI the first draft of the work-sample brief and its marking sheet. Choosing the task, setting the timebox and marking stay with the hiring manager.

Show the prompt and the checks

Describe: You are helping a hiring manager design a work sample for a Data Engineer (AI Platform) role. Context: I will paste the competency model, a real problem the team solved recently with anything confidential removed, the timebox, and our AI-tool policy for candidates. Produce: a candidate-facing brief (what to do, what to hand in, the time cap, how to request an adjustment, which AI tools are allowed and how to report their use); and a marking sheet with 4–6 criteria tied to named competencies, each with what a 1, a 3 and a 5 look like. Use British English and plain, inclusive wording. Do not design anything that needs more than the stated time. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the task fits the timebox, that every criterion maps to a competency, and that the brief gives all candidates the same starting materials.
Diligence: The hiring manager owns the task and the criteria, and people mark every submission. Do not paste candidate submissions into an AI tool for scoring; AI drafts the brief, people mark and decide.

H-EvaluateH-Evaluate can generate a job-specific assessment from your job description, so you don't have to build the task by hand.How H-Evaluate helps →

Don't miss: Asking candidates to design a data platform from a blank page. It rewards spare evenings, not the skill of finding why a running pipeline went wrong.

Framework

Work-sample test

A small, realistic slice of the job, marked against criteria written in advance. Give candidates equivalent materials and arrange reasonable adjustments where required.

Take a real problem the team solved recently and shrink it to fit the timebox.
Write the marking criteria first, tied to the competency model.
Timebox honestly — for take-home work, a few hours at most — and say so in the brief.
State the AI-tool policy in writing. Where the job permits these tools, use equivalent conditions in the assessment and evaluate how candidates verify their output.
Mark without CV details where you can, and have two people mark borderline submissions.

Why it works: A well-designed work sample gives direct evidence of job-relevant skill rather than a proxy for it, and gives candidates a realistic preview of the work.

Assessment rules for every candidate

State the planned time cap and how to request an adjustment. At the agreed stopping time, ask candidates to note what they would examine next and what they left out — never reward unpaid overtime.
Give every candidate the same starting materials, and use a second marker for borderline submissions.
Say in writing which AI tools are allowed and ask candidates to tell you how they used them.
Invite every candidate to ask for adjustments, and answer each request promptly.
Pay for anything the task costs the candidate to run, such as model or cloud usage.

The work sample

Find why a retrieval index serves stale and duplicated records

Timebox: Planned for up to three hours (adjustments available on request). At the agreed stopping time, write a short note on what you would examine next, and what you deliberately left out.

What the candidate hands in

Your diagnosis: which step is at fault, the evidence in the logs and sample that shows it, and why the symptoms appeared where they did.
The corrected transform for that step, with a short explanation of each change.
One data-quality check — what it tests, its threshold, and whether a failure blocks the load or only warns.
A few lines on how you would clean the records already in the index, and whether that needs a backfill or a replay.

Marking criteria (write these first)

Reaches a diagnosis in the merge step that is supported by the evidence in the brief — for example, updated=0 and deleted=0 on every run since the switch — rather than blaming the embedding retries or the source team.
Points to specific evidence, such as target rows climbing by exactly the source rows each night, P-1042 appearing twice, or P-0877 still active after its delete.
The fix handles both updated and deleted records and stays correct if the job runs twice.
The check would have failed on the bad load and is tied to the cause, not a generic row-count alarm.
The clean-up plan says what happens to records already embedded, and in what order.
The write-up is clear enough for an ML engineer who did not build the pipeline to follow.

Adjustments: Candidates can ask for more time, for the files in another format, or to work through the task live with an interviewer. Everything runs locally on the supplied files — no cloud account or paid service is needed.

Watch out for

Accepting a fix with no check attached. The data-quality check is half the task, because the next upstream change will come.

Compliance checkpointUSGreat BritainEU

In the US, covered employers must provide a reasonable accommodation to a qualified applicant with a disability unless it would cause undue hardship (ADA). In Great Britain, the Equality Act 2010 requires reasonable adjustments in recruitment. In the EU, the duty comes from national equality law. Don't ask for more medical information than the law allows.

Sources: EEOC — prohibited practices · Equality Act 2010 · checked 2026-09-25

Stage 8 of 13Required

0 of 3 done

Structured interviews

Structured interview loop

Who runs it: Interview panel (named in advance)
Done when: Every competency has a lead interviewer and at least one second source of evidence, with the same questions for every candidate.

Key tool

Interview questions by pillar

Free in this kit: Domain questions for every round. The other three pillars and the AI-fluency check are measured by H-Evaluate.

Free in this kit

Senior data engineer who marked the work sample · Pipeline reliability and reruns · Data-quality checks

1.Talk me through how you ruled out the embedding retries and the source team's export before settling on the merge step. What evidence moved you each time?
Pipeline reliability and reruns
Good answerNames concrete evidence for each step — retries affecting latency but not row counts, the export matching its change note, the same key appearing with several update times — and explains the order of their checks.
Weak answerWent straight to the answer and cannot say what they ruled out, or blames the upstream team without evidence.
2.Suppose tonight's run fails halfway and someone reruns it tomorrow on top of the partial load. Walk me through what your corrected step does, record by record.
Pipeline reliability and reruns
Good answerShows the merge on a stable key keeps the latest version, applies deletes once, and leaves the table identical after one run or two; mentions staging or a transaction so readers never see half a batch.
Weak answerAssumes reruns do not happen, or describes a fix that appends again on the second run.
3.Why did you choose that check over the other ones you considered, and what would a false alarm from it look like?
Data-quality checks
Good answerTies the check to the failure — for example uniqueness of the product key in the index-ready table, or active count reconciled with the source — and describes when it might fire wrongly and how they would tune or handle that.
Weak answerPicks a generic 'row count above zero' check, or cannot say what a false alarm would look like.

Optional follow-ups (1)

4.Which checks would you run on every load of a retrieval dataset, and which only weekly? Who should be alerted when each one fails?
Data-quality checks
Good answerSeparates cheap blocking checks (keys, nulls, freshness) from heavier ones (sampling text content, comparing with the source), and names an owner and an action for each.
Weak answerRuns every check everywhere with no thought for cost, or sends every alert to a shared channel nobody owns.

AI as an advantageAI boost4D framework

Delegate: Hand AI the drafting of probe questions and strong / weak anchors for one competency. Asking, listening and scoring stay with the interviewer.

Show the prompt and the checks

Describe: You are helping an interviewer prepare a structured technical interview for a Data Engineer (AI Platform) role. Context: I will paste one competency, its evidence description and the core question we ask every candidate. Produce: four probes in this fixed order — what would you do, why that choice, what else did you consider, how would you know it worked — tailored to the question; a one-line description of a strong answer and of a weak answer; and two follow-ups to use only if time allows. Use British English and inclusive wording. Keep everything about the work, nothing about the person. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the probes test the named competency rather than trivia, that the anchors describe observable evidence, and that no question touches a protected characteristic.
Diligence: Interviewers ask the same core questions of every candidate and score alone against the rubric. Interview notes and candidate names never go into an AI tool; AI drafts the questions, people interview and decide.

H-EvaluateH-Evaluate can add evidence on Cognitive, Situational judgement, Behavioural and AI fluency for your panel to review alongside these interviews.How H-Evaluate helps →

Don't miss: Scoring the deep-dive on whether the candidate's fix matches yours line for line, rather than whether their diagnosis and check hold up.

Framework

Structured technical interview

Every candidate gets the same job-specific questions, each interviewer leads on specific competencies, and every answer is probed with the same four follow-ups: what would you do, why, what else did you consider, and how would you know it worked.

Give each competency a lead interviewer, plus a second source of evidence — the work sample, another round or an independent marker. Avoid asking the same thing twice, but never let one person's view be the only evidence for a critical competency.
Ask the three core questions for your round, in order, for every candidate. Use an optional follow-up only if there is time.
Probe each answer: why that choice, what else they considered, and how they would know it worked — until you hear the candidate's own technical decision.
Take notes on evidence during the interview. Don't rate until it's over.
Fill in your interviewer sheet and submit it before you talk to any other interviewer.

Why it works: Structured interviews make answers comparable: every candidate faces the same job-related questions and is scored against the same anchors.

Interview rules

Use the same core questions in the same order for every candidate.
In the work-sample review, understand the candidate's decisions against the rubric — don't turn it into a debate about your preferred solution.
Avoid trivia disconnected from a real job decision. Use a realistic debugging, design or evaluation problem instead.

Watch out for

Letting the design round turn into a tour of tools. Bring it back to grain, merge keys, versions and who may read what.

Compliance checkpointUSUKEU

Don't ask about protected characteristics — such as age, disability, pregnancy or family plans, religion, race or nationality — unless applicable law permits the question for a specific, necessary, job-related purpose, and HR or counsel has confirmed that basis beforehand. Ask only whether the candidate can meet the role's work-authorisation requirements. If a candidate volunteers protected information, don't probe; record only what is lawfully needed, keep it separate and restrict access.

Sources: EEOC — prohibited practices · Equality Act 2010 · checked 2026-09-25

Where to find more interview questions

Use web question banks for inspiration only: ask every candidate the same job-related questions, check each one against this kit's competencies, and never ask about protected characteristics. Don't reuse this kit's work sample from a public bank — candidates can find questions that circulate online, so a widely posted question tests preparation more than skill.

Show 7 sites

US OPM — Structured interviews(opens in a new tab)
US federal guidance on building structured interviews, with a guide, training material and sample questions with rating scales.
FreeFor interviewers
Acas — Interviewing job applicants(opens in a new tab)
UK public guidance on preparing fair interviews, asking applicants the same questions and avoiding discriminatory ones.
FreeFor interviewers
Workable — Interview questions library(opens in a new tab)
Sample interview questions for employers, listed by job role, including machine learning, data and engineering roles.
FreeFor interviewers
Indeed for Employers — Interview questions(opens in a new tab)
Sample interview questions for employers by job title and skill, with notes on what to look for in answers.
FreeFor interviewers
Tech Interview Handbook(opens in a new tab)
Open-source software-engineering interview preparation: coding, system design and behavioural questions.
FreeFor candidates and interviewers
Chip Huyen — Machine Learning Interviews Book(opens in a new tab)
Free online book with knowledge questions on maths, computer science and machine learning, grouped by difficulty.
FreeFor candidates and interviewers
Blind — Interview questions channel(opens in a new tab)
Anonymous posts by tech workers about questions they report being asked. Unverified; some content needs sign-in.
Free, some content needs sign-inCandidate reports, unverified

Links checked 2026-10-05. External sites; we don't control their content.

Stage 9 of 13Required

0 of 2 done

Compare evidence

Scoring, calibration and debrief

Who runs it: Hiring manager (chairs) + panel
Done when: A documented hire / no-hire decision traceable to scored evidence against the competency model.

Key tool

Interactive scorecard

Score one candidate against the rubric. Everything stays in your browser. Download it as a spreadsheet for your records.

Pipeline reliability and reruns Must-have

Weight

What do 1, 3 and 5 look like?

1: Jobs assume a single clean run; a rerun or backfill duplicates or loses records.
3: Makes a job idempotent when asked and handles the common rerun case.
5: Designs every load to be safe to rerun, replay and backfill from the start, and can show it on paper.

Data-quality checks Must-have

Weight

What do 1, 3 and 5 look like?

1: No checks beyond 'the job succeeded'.
3: Adds sensible checks for nulls, keys and counts; not always clear which should block a load.
5: Chooses checks from the ways the data has actually failed, sets blocking and warning levels deliberately, and names who acts on each.

Lineage and dataset versioning Must-have

Weight

What do 1, 3 and 5 look like?

1: Cannot say where a record in a dataset came from or reproduce an old version.
3: Can trace records with some effort; versions some datasets.
5: Records lineage and versions by default, so a training set or index can be traced and rebuilt on request.

Data modelling and SQL Must-have

Weight

What do 1, 3 and 5 look like?

1: Unclear about grain; joins that fan out go unnoticed.
3: Writes correct SQL and states the grain when prompted.
5: Designs grain and keys that survive upstream change, and spots fan-out or key drift before it ships.

Access control and personal data Must-have

Weight

What do 1, 3 and 5 look like?

1: Treats all data as open to anyone on the team; no thought for deletion.
3: Restricts obvious sensitive fields and follows the policy when reminded.
5: Scopes access by default, keeps restricted fields out of AI datasets unless approved, and handles a deletion request by locating each copy, applying the retention policy, documenting the result and escalating unresolved model effects.

Cost and freshness trade-offs Can learn

Weight

What do 1, 3 and 5 look like?

1: No idea what a load or embedding run costs; freshness is whatever the schedule happens to be.
3: Knows the main cost drivers and asks about freshness needs.
5: Prices the options, agrees freshness targets with users, and explains the trade-off clearly to non-engineers.

Weighted score– / 5

AI as an advantage4D framework

Delegate: Hand AI the drafting of a blank debrief worksheet and agenda from the competency rubric alone. People add the evidence locally; the scores and the decision stay with the panel.

Show the prompt and the checks

Describe: You are helping a hiring manager prepare a debrief for a Data Engineer (AI Platform) role. Context: I will paste the competency rubric only — the competencies, their weights and their 1, 3 and 5 anchors. Produce: (1) a blank debrief worksheet with one row per competency and empty columns for each interviewer's independent score, the evidence they cite, the spread of scores and an 'evidence gap?' box; (2) a 45-minute agenda: confirm every scorecard is in and reveal all scores at once, discuss the widest spreads using evidence only, review any must-have scored 1, then look at the weighted total and decide (hire, no hire or one targeted follow-up), and record the reasons; and (3) neutral questions the chair can ask to draw out evidence rather than adjectives. Leave every evidence and score cell empty. Do not suggest a score, a rating or a recommendation. Use British English. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the worksheet follows your rubric exactly — same competencies, weights and anchors — and that the agenda keeps independent scores hidden until the reveal.
Diligence: Interviewers fill in the evidence and scores locally, alone, before the debrief, and the panel decides together. Candidate notes and personal data never go into an AI tool; AI never scores, ranks or compares candidates.

H-EvaluateH-Evaluate gives you a report on each candidate's assessment evidence to bring into this debrief.How H-Evaluate helps →

Don't miss: Letting a strong score on SQL cover for a weak one on reruns. A pipeline that duplicates on replay will undo good modelling.

Stage 10 of 13Optional

0 of 2 done

Check references

References and background checks

Who runs it: Recruiter (references) + HR (checks)
Done when: At least two references that test specific competencies, plus only the checks that are lawful and relevant to the role.

Use when:: It is lawful where you hire, and specific evidence from a referee would change the decision.
Skip when:: Local law or company policy rules it out.

Key tool

Ask each referee

1If you depended on data from [candidate]'s pipelines, what happened the last time it arrived late or wrong?
2Which check or safeguard they added is still running today?
3How did they handle requests for access to sensitive data?

AI as an advantage4D framework

Delegate: Hand AI the drafting of reference questions aimed at the competency the panel was least sure about. Choosing referees, making the calls and interpreting the answers stay with the recruiter.

Show the prompt and the checks

Describe: You are helping a recruiter write competency-based reference questions for a Data Engineer (AI Platform) role. Context: I will paste the competency model and the one or two competencies where our evidence is thinnest — no candidate details. Produce: 3–4 questions per competency that ask for specific examples ('tell me about a time…') rather than ratings; an opening question about how the referee worked with the person and for how long; and a closing question about what their next manager could do to help them do their best work. Use British English and inclusive wording. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the questions ask for examples rather than opinions, that they stay on job-related competencies, and that none asks about health, family or other protected matters.
Diligence: References are taken with the candidate's consent and interpreted by people. Referee answers and candidate details never go into an AI tool; AI drafts the questions, people decide.

Don't miss: Hearing 'the data was always there' and moving on. Ask what happened the last time it was not.

Framework

Competency-based referencing

Treat references as a last structured conversation about the candidate's work, aimed at the competencies where your evidence is thinnest.

With the candidate's consent, ask for two people who saw their relevant work directly — ideally a former manager and a peer.
Accept suitable alternatives if a manager or peer isn't available, and never penalise a candidate for a former employer's no-reference policy.
Aim your questions at the one or two competencies the debrief was least sure about.
Ask for examples, not ratings. 'Tell me about a time they…' works here too.
Set the timing of each background check by the law and policy that apply.

Why it works: Generic reference questions tend to produce little job-related evidence. Asking for specific examples tied to the role's competencies turns referencing into something you can use.

Core reference questions and checks

Open with: 'How did you work together, and for how long?' — it tells you how much weight the rest can carry.
Ask: 'Tell me about a time something they owned went wrong. What did they do?'
Close every reference with: 'What should their next manager do to help them do their best work?'
Verify only claims that were material to the decision — don't verify a degree the role never required.
Run criminal-record checks only where the role justifies them and local law allows; many roles don't need one.
Schedule formal work-authorisation checks at the legally required time: before employment starts in the UK; for US Form I-9, Section 1 by the first day of paid work and Section 2 within three business days.

Reference questions specific to this role

1.If you depended on data from [candidate]'s pipelines, what happened the last time it arrived late or wrong?
Listen for: How quickly they noticed, how they told consumers and what they changed afterwards.
2.Which check or safeguard they added is still running today?
Listen for: A specific, lasting piece of work rather than a general compliment.
3.How did they handle requests for access to sensitive data?
Listen for: Evidence that they scoped access thoughtfully rather than either granting everything or blocking everything.

Compliance checkpointUS

If a consumer reporting agency provides a background report, the Fair Credit Reporting Act requires a clear, stand-alone written disclosure and the candidate's written authorisation before you order it. Before rejecting someone because of the report, give them a copy and the FCRA summary of rights and a reasonable opportunity to review and dispute it before any adverse action; if you still reject, send a final adverse-action notice. State and local 'fair chance' laws may delay criminal-history questions or checks until after a conditional offer.

Sources: FTC — Background checks: what employers need to know · FTC — Using consumer reports: what employers need to know · checked 2026-09-25

Stage 11 of 13Required

0 of 2 done

Agree the offer

Offer, negotiation and close

Who runs it: Hiring manager + recruiter
Done when: An accepted written offer within the approved band, with a start date.

Key tool

Build the offer around

•Ownership: the datasets every AI feature reads, with a say in how they are built.
•A budget for warehouse and embedding compute they can direct for backfills and experiments.
•Time and support to write up or present a pipeline problem they solved.
•A clear explanation of the level offered and what the next one would require.

AI as an advantage4D framework

Delegate: Hand AI the first draft of the offer message built around what the candidate said matters to them. The figure, the approval and the conversation stay with the hiring manager.

Show the prompt and the checks

Describe: You are helping a hiring manager draft an offer message for a Data Engineer (AI Platform) role. Context: I will paste three general priority categories (for example: learning, flexibility, scope) — not the candidate's words, notes or personal details; the parts of the offer; and the start-date options — without the salary figure. Produce: a warm, plain-English message that links each part of the offer to one of their priorities, says what happens next and when the written offer follows, and invites questions. Use British English and inclusive wording, and leave [brackets] for figures and names. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that nothing promised is outside policy or the approved band, that every claim about the role is true, and that the message reads as personal rather than templated.
Diligence: The hiring manager places the offer in the band using the same pre-set factors for everyone and makes the verbal offer personally. Figures and personal details are filled in by people, never pasted into AI.

Don't miss: Leaving on-call expectations for data incidents unstated until after acceptance.

Framework

Close plan (motivators → offer → objections)

Build the offer around what the candidate told you they care about, anticipate their objections, and make sure the verbal and written offers match.

List the candidate's top three motivators from your notes at every stage.
Make the verbal offer personally, and link each part of it to a motivator.
Ask what else they are weighing and when they have to decide.
Place the offer in the band using the same pre-set, job-related factors for everyone, and record why.
Send the written offer promptly after the verbal one.

Why it works: A close plan ties the offer to the candidate's stated priorities and makes approvals explicit before the final interview.

Offer checklist and negotiation

Stay inside the approved band. If the candidate is worth more, re-level them with evidence rather than stretching the band.
Trade in things that cost less than salary where policy allows: start date, learning budget, working pattern.
Before the written offer, ask: 'What would make this an easy yes?'
Agree approval and the pay band before the final interview, so the offer isn't held up.
The hiring manager makes the verbal offer; the written offer follows within a working day.
Record which pre-set factors placed the offer where it is in the band.
Tell unsuccessful finalists personally, without avoidable delay, with brief feedback.

What will close this candidate

Ownership: the datasets every AI feature reads, with a say in how they are built.
A budget for warehouse and embedding compute they can direct for backfills and experiments.
Time and support to write up or present a pipeline problem they solved.
A clear explanation of the level offered and what the next one would require.

Compliance checkpointEUUS

Use objective, gender-neutral, pre-agreed factors to place candidates within the band, apply them consistently and record the decision as an internal pay-equity control. Check the national law implementing Directive (EU) 2023/970 and any local record-keeping rules; this recommendation is good practice, not a universal statutory documentation rule.

Sources: EU Pay Transparency Directive 2023/970 · checked 2026-09-25

Stage 12 of 13Required

0 of 2 done

Help them settle in

Pre-boarding and 30-60-90 onboarding

Who runs it: Hiring manager + buddy + HR
Done when: A new hire who has worked through a written 30-60-90 plan tied to the 12-month outcomes, with a check-in at each milestone.

Key tool

Before day one
Laptop, repository access and read access to logs and the warehouse in place before day one.
Days 1–30: learn
Meets the ML engineers, analysts and product owners who consume each AI dataset.
Days 31–60: contribute
Adds blocking quality checks to the pipeline with the most visible incidents.
Days 61–90: own
Owns one AI dataset end to end, with a freshness target, checks and a version history.

AI as an advantage4D framework

Delegate: Hand AI the first draft of the 30-60-90 plan from the 12-month outcomes. Agreeing the plan with the new hire stays with the manager.

Show the prompt and the checks

Describe: You are helping a hiring manager draft a 30-60-90 day onboarding plan for a new Data Engineer (AI Platform). Context: I will paste the 12-month outcomes from the intake meeting, the systems and people the hire will work with, and anything they will need before day one. Produce: a pre-boarding checklist (equipment, access, a named buddy, a first-week schedule); then three sections — days 1–30 learn, days 31–60 contribute, days 61–90 own — each with 3–4 concrete, checkable expectations and one milestone check-in question. Use British English and plain wording. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that each expectation is realistic for a new starter, ties back to a 12-month outcome, and offers the hire support rather than a test.
Diligence: The manager agrees the plan with the new hire in week one and owns the check-ins. The new hire's personal details stay out of the prompt.

Don't miss: Giving a new hire broad access to restricted datasets on day one because it is quicker than scoping it.

Framework

30-60-90 day plan

Three phases — learn, contribute, own — with written expectations for each, agreed with the new hire in week one.

Before day one: equipment, access, a named buddy and a first-week schedule.
Days 1–30, learn: systems, people and the current state of the work.
Days 31–60, contribute: a first scoped piece of work shipped with support.
Days 61–90, own: an area of responsibility with clear outcomes.
Hold a check-in at each milestone against the written plan.

Why it works: Hiring doesn't end at the signature. A written onboarding plan makes expectations and support visible to the new hire and their manager.

Before day one
- Laptop, repository access and read access to logs and the warehouse in place before day one.
- Access to restricted datasets scoped to the first pipelines they will own, with a request route for more.
- A named buddy who currently runs one of the AI data pipelines.
- Reading: the list of sources and AI datasets, recent data incidents, and the data-protection policy.
Days 1–30: learn
- Meets the ML engineers, analysts and product owners who consume each AI dataset.
- Draws the map from each source system to each training set and index, marking which steps have checks.
- Merges one modest fix to a live pipeline so the release process is learned on something low-risk.
Days 31–60: contribute
- Adds blocking quality checks to the pipeline with the most visible incidents.
- Makes one pipeline safe to rerun and proves it with a test replay.
- Records lineage for the main retrieval index or training set.
Days 61–90: own
- Owns one AI dataset end to end, with a freshness target, checks and a version history.
- Runs a real backfill or deletion request through the pipeline and documents it.
- Presents the cost and freshness of each AI dataset to the team, with one proposed change.

Compliance checkpointUSUK

US: the employee completes Form I-9 Section 1 no later than their first day of paid work, and the employer completes Section 2 within three business days after that day (on the first day if the job lasts less than three business days). UK: complete the prescribed right-to-work check before employment starts, and apply it the same way to everyone.

Sources: USCIS — Form I-9 instructions · GOV.UK — Check a job applicant's right to work · checked 2026-09-25

Stage 13 of 13Required

0 of 3 done

Measure time to hire

Measure the process: mean time to hire

Who runs it: Recruiter / talent lead + hiring manager
Done when: A stage-by-stage timing record for this hire, the slowest step named, and one change agreed before the next requisition opens.

Key tool

Record when the hire accepts

•Time to hire: days from the successful candidate entering the pipeline (application or first contact) to the accepted offer. Average it across hires for mean time to hire.
•Time to fill, recorded separately: days from requisition approval to the accepted offer.
•Days per stage, from the stage's first action to its last — and whether the time was spent waiting or working.
•Offer-acceptance rate: offers accepted divided by offers made.
•Where candidates dropped out, and the reason you were given.
•Days spent preparing anonymised or synthetic logs and sample output before the work sample could be sent.

Check in at 90 days whether the hire is meeting the outcomes in the job description — that tells you whether the process picked well.

Read: mean time to hire Read: time to hire vs time to fill

AI as an advantageAI boost4D framework

Delegate: Hand AI the job of building a blank timing worksheet and the instructions for reading it. Entering dates, working out the timings and deciding what to change stay with the recruiter and the hiring manager.

Show the prompt and the checks

Describe: You are helping a recruiter and a hiring manager set up a timing review for a Data Engineer (AI Platform) hire. Context: I will list the stage names we used, in order (for example requisition approved, sourcing, screening, recruiter call, work sample, interviews, debrief, references, offer accepted). Produce: (1) a blank worksheet with one row per stage and columns for start date, end date, days in stage, days spent waiting (scheduling, approvals, no reply) and number of candidates who entered and left; (2) step-by-step instructions for us to work out, from our own records, time to hire (the successful candidate's entry to the pipeline, by application or first contact, to the accepted offer; average it across hires for mean time to hire) and, separately, time to fill (requisition approval to the accepted offer); (3) five questions we should ask about the slowest stage, separating waiting from working; (4) a short template for recording one change to make before the next hire. Use British English. Do not include benchmarks or example figures. Ask me questions if anything is missing. Rule: do not ask for, and I will not paste, candidate names, interview notes, CVs, submissions or any other personal data. If any appears, stop and tell me to remove it.
Discern: Check that the worksheet covers every stage you actually ran, that the two definitions are not mixed up, and that the questions point at the process rather than at any person.
Diligence: People fill in the dates and do the arithmetic from their own records, choose the one thing to fix and write it down. No dates, names or details about any candidate go into the prompt.

Don't miss: Building the work sample from real production logs and then waiting on a privacy review each time, instead of preparing synthetic data once.

Framework

Mean time to hire, by stage

Time to hire is the number of days from the successful candidate entering your pipeline (their application or your first contact) to their accepted offer; mean time to hire is that figure averaged across hires. Time to fill — requisition approval to acceptance — is a separate measure. Record both per stage, so you can see where the time went rather than only the total.

When the hire accepts, write down the date each stage started and ended, and the day the successful candidate entered the pipeline.
Work out the days per stage, the time to hire and the time to fill, and mark the slowest stage.
Note the offer-acceptance rate and the stage where most candidates dropped out.
Separate waiting time (scheduling, approvals, silence) from working time, and decide which one to shorten.
Fix one thing before the next hire, and write down what you changed.

Why it works: A single total hides where the delay sits. Per-stage timing points at the one step to fix, and the acceptance rate and drop-out points show whether candidates kept pace with the process.

Record for every hire

Time to hire: days from the successful candidate entering the pipeline (application or first contact) to the accepted offer. Average it across hires for mean time to hire.
Time to fill, recorded separately: days from requisition approval to the accepted offer.
Days per stage, from the stage's first action to its last — and whether the time was spent waiting or working.
Offer-acceptance rate: offers accepted divided by offers made.
Where candidates dropped out, and the reason you were given.
Check in at 90 days whether the hire is meeting the outcomes in the job description — that tells you whether the process picked well.

What to log for this role

Days spent preparing anonymised or synthetic logs and sample output before the work sample could be sent.
Whether the data-protection reviewer needed to see the work-sample files, and how long that took.
Whether candidates who dropped out did so before opening the work-sample files or after reading them, and what that suggests about the brief.

Where this role's hiring slows down

Watch for the work-sample data being rebuilt from production for every search instead of kept as a reusable synthetic set.
Watch for the data-platform design round waiting on one staff engineer, with no second interviewer able to score access-control answers.
Watch for consumers of the data, such as ML engineers, being left out of the panel and then questioning the decision afterwards.

Compliance checkpointUKEU

Under the EU and UK GDPR, tell candidates how long you will keep their data, or the criteria used to decide. Give that notice when you collect data from them directly, or within the Article 14 time limit when it comes from another source (such as a referral or a sourcing tool). Delete or anonymise data when it is no longer needed, keeping only what a documented lawful purpose still requires.

Sources: GDPR — Regulation (EU) 2016/679, Articles 5, 13 and 14 · ICO — UK GDPR guidance · checked 2026-09-25

General information, not legal advice. Rules depend on the employer, the role and where the work is done — confirm what applies with HR or counsel.

Frequently asked questions

What does a data engineer for an AI platform do?

They build and run the pipelines that feed models and retrieval systems: preparing training data, loading and refreshing search indexes, versioning datasets, checking quality before data is used, and controlling who can read what. The focus is on data that is correct, current and permitted, rather than on training the models themselves.

How is this different from a general data engineer?

The core skills are the same — SQL, reliable pipelines, data modelling, quality checks. What changes is the consumer. Errors surface as wrong answers from a model or assistant rather than a wrong dashboard figure, and extra work appears: re-embedding, dataset versions for training, and deletion requests that must reach indexes and training copies.

Data engineer or machine learning engineer — which do we need?

If the open problem is whether the model is good enough, look at a machine learning engineer. If it is whether the model is reading the right, current, permitted data, look at a data engineer. Many teams need both; write down who owns the boundary, such as feature pipelines and training-set preparation.

What is a good work sample for this role?

A small pipeline that has started producing bad output after an upstream change, with dated log lines and sample output rows in the brief. Ask the candidate to find the cause, fix the step and add one check that would have caught it, within a stated limit of up to three hours. It tests diagnosis, rerun safety and quality habits in one task.

Should the work sample allow AI tools?

Yes, where your policy allows AI tools on the job. Ask candidates to say which tools they used and how they checked the result. Engineers in this role often have AI draft SQL or transforms, so checking that output is part of what you are assessing.

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