Data & AI

Data Engineer mock interview

Pipelines, not models. Expect idempotency, late-arriving data, backfills and schema evolution — and at least one question about what happens when a job silently produces wrong numbers for a week.

Data loops test two things that pull against each other: rigour with methods, and the judgement to know when a rough answer is the right one. Candidates who are strong on one are often visibly weak on the other.

Expect questions where the data is deliberately insufficient. Stating the assumption you are making, and what would change your answer, is most of the mark.

The rounds you can practise

Each round is run by the interviewer built for it, with its own scoring axes — not one generic interviewer asked to change subject.

Metrics, experiments, causal reasoning, and decisions under uncertainty.

Scored on Analytical communication · Data & inference rigor · Decision quality

Metrics, experiments, causal reasoning, and decisions under uncertainty.

Scored on Analytical communication · Data & inference rigor · Decision quality

Hands-on problem solving, code quality, debugging, and engineering judgment.

Scored on Problem-solving communication · Engineering correctness · Adaptability & craft

System design, trade-offs, and war stories from production.

Scored on Technical communication · Engineering depth · Ownership & collaboration

Metrics, experiments, causal reasoning, and decisions under uncertainty.

Scored on Analytical communication · Data & inference rigor · Decision quality

Data Engineer interview questions

Six you can expect, in the register interviewers actually use. Answer them out loud before you read the next section — reading a question and answering one are different skills, and only the second is marked.

  1. A daily job has been silently producing wrong numbers for a week. What happens?
  2. How do you handle late-arriving data?
  3. Design a pipeline that must be safe to re-run.
  4. How do you manage a schema change upstream that you do not control?
  5. When would you choose batch over streaming, and what does that cost you?
  6. How do you backfill two years of data without taking the warehouse down?

The same question, answered badly and well

The gap between these two is most of your score, and it is easier to see than to be told.

A daily job has been silently producing wrong numbers for a week. What happens?

What loses marks

Fixing the job. It addresses the cause and leaves a week of bad numbers sitting in every downstream dashboard and model.

What scores

Stop the spread, quantify the blast radius, tell the consumers before they find out, then backfill, then add the check that would have caught it on day one. Silence is the defining word in the question and the answer has to deal with it.

Why Data Engineer candidates get cut

The post-mortem nobody sends you. These are specific to this loop rather than general interview advice.

Pipelines that are not idempotent, and no recognition of why that matters when a re-run happens at 3am.
No data quality layer — correctness treated as something that happens elsewhere.
Answering as an analyst: modelling and dashboards rather than reliability and recovery.

Start with a general round

For Data Engineer, a general round runs with Marcus metrics, experiments, causal reasoning, and decisions under uncertainty. It is the fastest way to find out which round you actually need to work on. The first session is free.

Practise a Data Engineer interview →

Question guides

Before you practise, it is worth reading how the common rounds are marked: tell me about yourself, behavioral questions and STAR, and system design.

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