Data Analyst Interview Questions 2026
Questions that test SQL fluency, statistical thinking, and the ability to turn raw data into stakeholder decisions.
10 questions3 categoriesWith answer hints
Technical
4 questions1What is the difference between a GROUP BY and a PARTITION BY clause in SQL?
Hint: GROUP BY collapses rows into aggregated groups. PARTITION BY (window function) keeps all rows but applies the aggregation relative to each row's partition — useful for running totals, ranks, and moving averages.
2Explain the difference between a left join and an inner join. When would a left join produce rows an inner join would not?
Hint: Inner join: only rows matching in both tables. Left join: all rows from the left table, NULLs where right table has no match. Use left join to find unmatched records (e.g., users who never made a purchase).
3What is a cohort analysis, and what business questions is it best suited to answer?
Hint: Cohort analysis groups users by a common time-based attribute (signup week) and tracks behavior over time. Best for retention, LTV evolution, and testing whether product changes affected a specific cohort differently.
4How do you detect and handle outliers in a dataset before analysis?
Hint: Detection: IQR rule (>1.5×IQR from quartiles), z-score (>3σ), or visualization (box plots, scatter plots). Handling depends on cause: data error (remove), genuine outlier (cap or separate analysis), or signal (investigate first).
Behavioral
3 questions5Tell me about a time your analysis led to a significant business decision. What was the impact?
Hint: Show the decision-enabling arc: what question you were asked, how you structured the analysis, how you communicated uncertainty, and what action was taken. Quantify the outcome where possible.
6Describe a time you found an error in data you had already reported. How did you handle it?
Hint: Show intellectual integrity: proactive disclosure, rapid assessment of impact, corrected analysis, and what process change prevented recurrence (data validation checks, peer review).
7Tell me about a stakeholder who kept requesting new cuts of the same data without acting on any of it. How did you handle it?
Hint: Recognizing analysis paralysis or unclear decision criteria is an analyst superpower. Show how you redirected to the underlying decision, proposed a recommendation format, and moved from exploration to action.
System Design
3 questions8Design a metrics framework for a subscription business that helps leadership understand health at a glance.
Hint: Cover north star metric, acquisition (CAC, CVR by channel), engagement (DAU/MAU, feature adoption), retention (monthly cohort retention, churn rate), and monetization (MRR, expansion revenue, LTV).
9How would you build a self-service analytics platform for 200 non-technical business users?
Hint: Cover semantic layer (business-friendly field names, pre-built metrics), governed dataset access, training program, usage monitoring, and a feedback loop to prioritize curated dashboards for high-demand questions.
10Design an experiment analysis pipeline for a product team running weekly A/B tests.
Hint: Cover event logging spec, assignment table structure, statistical testing (t-test, Mann-Whitney for non-normal), sample ratio mismatch detection, novelty effect window, and automated experiment report generation.
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View Data Analyst RoadmapQuestions reflect commonly asked interview topics for Data Analyst roles across companies of various sizes. Hints summarize what strong answers typically cover — use them as a preparation guide, not a script.