Data & AI

Data Analyst mock interview

Analyst loops test whether you can turn a vague business question into a specific, answerable one. SQL is table stakes; framing the question and communicating the caveat is what separates candidates.

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 Analyst 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 stakeholder asks why revenue is down. Where do you start?
  2. Turn 'are our users happy?' into something you can actually measure.
  3. Two dashboards disagree on the same number. What now?
  4. Write a query to find users whose activity dropped by half month over month.
  5. How do you present a finding nobody wants?
  6. What caveat do you always attach to your numbers?

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 stakeholder asks why revenue is down. Where do you start?

What loses marks

Going straight to SQL. It produces a number fast and usually answers a question nobody asked.

What scores

Scope it first — which revenue, over what period, compared to what, and what decision hangs on the answer — then segment before you explain. Framing the question is most of the job and almost all of the interview.

Why Data Analyst candidates get cut

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

SQL fluency with no framing. It is table stakes here, not a differentiator.
Presenting a number without the caveat, or with so many caveats that no decision is possible.
Never asking what the stakeholder intends to do with the answer.

Start with a general round

For Data Analyst, 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 Analyst 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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