Data & analytics
Data Analyst interview practice
Clear, evidence-backed answers for the work this role actually does. The question bank is free; the AI mock interview builds its follow-ups from your resume and this role.
Practise this roleWhat this role evaluates
Prepare one real example per topic: what you owned, the constraints, the options you rejected, what you did and a result someone could verify.
- SQL analysis
- metric definitions
- business attribution
Starter question bank
Eight questions worth preparing
Public practice prompts, not leaked questions from any employer. Pick one for a 10-minute drill with follow-ups and a report.
- 01As a Data Analyst, walk me through a real project that shows your SQL analysis. What did you personally own?Drill
- 02Tell me about a time metric definitions competed with a hard deadline. What did you prioritise and what did you give up?Drill
- 03Describe a time your work on business attribution fell short. How did you find the cause and what changed afterwards?Drill
- 04How would you explain a metric definitions decision to a colleague outside your field, and what if they disagreed?Drill
- 05You inherit a project with problems in SQL analysis. What evidence do you gather and what do you do in the first two weeks?Drill
- 06Which achievement on your resume best shows you are ready for a Data Analyst role? Separate your part from the team's.Drill
- 07Before answering a case question about business attribution, what would you clarify with the interviewer and why?Drill
- 08Tell me about a mistake you made in SQL analysis. What do you do differently now?Drill
In-depth role guide
Scenarios and answer checkpoints
Interviewers are looking for practitioners who can translate ambiguous business questions into rigorous, reproducible data queries.
Scenario 1
The marketing team claims a specific campaign caused a surge in revenue, but the raw logs show high bounce rates. How do you investigate this discrepancy?
A strong answer will
- Define the specific attribution model used to link user behavior to revenue.
- Identify potential data pipeline issues like duplicate logs or bot traffic.
- Propose a clean metric definition that filters out non-human noise.
Scenario 2
Explain how you would validate a new metric for customer retention when stakeholders have conflicting definitions of what constitutes an active user.
A strong answer will
- Synthesize stakeholder needs into a single, technical metric definition.
- Write a complex SQL query utilizing window functions to track user cohort behavior.
- Justify the choice of time-window and churn criteria based on business context.
Common mistake: The most common mistake is presenting findings without sanity-checking the source data; always explain how you verified the accuracy of your query results.
Free three-question practice
Speak first. Then write down the evidence.
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Question 1 of 3
As a Data Analyst, walk me through a real project that shows your SQL analysis. What did you personally own?
Fundamentals the interviewer probes
- SQL fluency: joins, window functions, CTEs, performance
- Defining metrics precisely and spotting metric gaming
- Data quality checks and reconciliation
- Dashboard design: what to show, what to cut
- A/B test reading and common pitfalls
- Turning analysis into a recommendation with a decision owner
Follow-ups to expect
- “Walk me through a query you wrote that changed a decision. What did the data look like and what did you find?”
- “Tell me about a metric that looked good but was wrong.”
- “How do you check a number before it goes to leadership?”