Practice Data Analyst interview questions on SQL analysis, metric definitions, business attribution. Upload your resume and target job for a personalized AI interview, transcript and feedback.
What this interview evaluates
SQL analysis
metric definitions
business attribution
Practice questions
As a Data Analyst, describe a real project that demonstrates SQL analysis. What did you personally own?
In a Data Analyst role: Two dashboards report different conversion rates for the same week. How would you reconcile the definitions and data? Explain how SQL analysis informs your decision.
When metric definitions conflicts with a delivery deadline, how do you prioritize? What would you give up?
Describe a time when business attribution fell short. How did you diagnose the cause and improve the outcome?
How would you explain a decision about metric definitions to a non-specialist colleague and handle disagreement?
You inherit a project with problems in SQL analysis. What evidence and actions would you prioritize in the first two weeks?
Which achievement on your resume best demonstrates readiness for a Data Analyst role? Separate your contribution from the team's.
What conditions or constraints about business attribution would you clarify with your interviewer?
Prepare your evidence
For each topic, prepare one real example: your responsibility, constraints, alternatives, actions, measurable results and lessons. The personalized question bank is generated from your resume and target job after sign-in.
Prepare to turn an ambiguous business question into a metric, a defensible query and a decision. Bring an analysis where your data checks changed the conclusion.
An experiment increases click-through rate but reduces paid conversion. Would you recommend shipping?
Define the primary metric, guardrails and unit of randomization.
Check uncertainty, sample balance, duration and segment effects.
Explain the tradeoff and what further evidence would change your decision.
Joining an orders table to events doubles reported revenue. How do you debug the query?
State the grain and unique key of each table.
Find one-to-many joins and compare counts before and after each join.
Aggregate or deduplicate at the intended grain and reconcile totals.
Common mistakes
Do not treat correlation as causation or a dashboard as a business outcome. Explain denominators, missing data and the limits of your conclusion.
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