Data Analyst Interview Questions

SQL questions

Most data analyst interviews include at least one SQL problem, regardless of seniority level. The complexity varies from straightforward aggregations to multi-step transformations using window functions.

What to expect by experience level

• Fresher to 1 year: SELECT with WHERE, GROUP BY, HAVING, ORDER BY; basic INNER JOIN and LEFT JOIN
• 1–3 years: Multi-table JOINs, subqueries, NULL handling, CASE statements, date functions
• 3+ years: Window functions (ROW_NUMBER, RANK, LAG, LEAD, SUM OVER), CTEs, query performance considerations

What interviewers are assessing

• Can you translate a business question into a correct query without being told the exact SQL structure?
• Do you consider edge cases — NULLs, duplicates, date boundary conditions?
• Do you think out loud and explain your approach before writing?
• Can you refine your initial query when the interviewer points out an issue?

Example SQL prompts

• "Find the top 3 products by revenue in each region."
• "Write a query to identify users who made a purchase in January but not in February."
• "Calculate month-over-month retention for a cohort of users who signed up in Q1."
• "Find the second-highest salary in each department."
• "Detect duplicate entries in a transactions table and return only the most recent."

Analytical and problem-solving questions

These questions assess structured thinking and analytical process, not just technical knowledge. They are often open-ended and have no single correct answer.

Common question formats

• "How would you measure the success of [feature launch / new product / campaign]?"
• "Our sign-up rate dropped 15% last week. Walk me through how you would investigate."
• "Design a dashboard for the sales team. What metrics would you include and why?"
• "How would you decide whether to continue or cut a low-performing marketing channel?"

What interviewers are assessing

• Do you define the problem and clarify assumptions before jumping to a solution?
• Can you propose and evaluate multiple hypotheses rather than the first one you think of?
• Do you distinguish between correlation and causation when interpreting results?
• Do you consider data availability, sample size, and confounding variables?
• Can you communicate a structured answer clearly within a few minutes without prompting?

Data interpretation questions

You may be shown a chart, table, or dashboard screenshot and asked to interpret what you see. These questions test whether you can draw defensible conclusions from data rather than just describe what is on the screen.

• "What stands out in this chart?" — interviewers want: identification of anomalies, seasonality, trends, and the questions they raise
• "Why might this metric have changed?" — interviewers want: multiple hypotheses, prioritised by plausibility, with data needed to test each
• "Is this change statistically meaningful?" — interviewers want: basic sense of sample size and variation, not necessarily a formal test
• "What additional data would you need before drawing a conclusion from this?"

Prepare to discuss how you handle conflicting signals across metrics, and when you would and would not trust a given data source.

Business communication and stakeholder questions

Data analyst roles require translating technical findings to people who do not think in SQL or statistics. Interviewers assess this through direct questions about how you communicate.

• "How do you explain a technical finding to a non-technical stakeholder?"
• "Describe a project where your analysis changed a business decision."
• "What do you do when stakeholders disagree with your numbers or conclusions?"
• "How do you handle requests for analysis that you think are asking the wrong question?"

Behavioral questions

Behavioral questions follow the STAR format: Situation, Task, Action, Result. The best answers are specific, personal, and honest — including what went wrong when relevant.

• "Tell me about a time you worked with messy, incomplete, or unreliable data. What did you do?"
• "Describe a situation where your initial analysis was wrong. How did you discover it and what happened next?"
• "Tell me about a time you had to prioritise competing analysis requests. How did you decide what to do first?"
• "Tell me about a time you influenced a business decision with your analysis."
• "Describe a situation where you had to deliver findings the team did not want to hear."

Portfolio and project discussion

Interviewers frequently ask about past analytical projects to understand how deeply you have actually worked with data.

• Be specific about what you personally contributed versus what the broader team did
• Know the business context — what problem were you solving? Who used the output?
• Know the data — what was the source, scale, and key limitations?
• Know the outcome — was the analysis acted on? What decision did it inform?
• Be prepared for the follow-up: "What would you do differently if you ran this project again?"

How to prepare

1. 1Practice SQL problems by hand — not in an IDE with autocomplete or query execution
2. 2Build 4–5 specific STAR stories covering: ownership, analytical difficulty, cross-functional communication, and at least one situation where something did not go as planned
3. 3Review the company's public metrics, product, or recent announcements before the interview — it signals genuine engagement with their work
4. 4Research their data stack if visible in the job posting (dbt, Airflow, Looker, Metabase, Tableau, Power BI) — familiarity with their environment signals practical readiness
5. 5Prepare two or three specific questions about the team's data infrastructure and what types of decisions your analysis would inform