Data analyst interview questions are technical and behavioral prompts that test whether you can pull clean data, analyze it correctly, and explain what it means to people who don't work with data. Expect a mix of SQL problems, statistics, a business case, tool questions, and stories about past projects.
Quick answer: Most data analyst interviews cover four areas: SQL and technical skills, statistics and analysis, a business or case scenario, and behavioral questions about how you work. You'll usually write or talk through at least one SQL query and explain how you'd measure or investigate a metric. Prepare by practicing SQL out loud, rehearsing two or three project stories, and being ready to reason through an ambiguous business question.
What SQL and technical questions will they ask?
SQL is the core screen for most analyst roles. You'll often write queries live or read one and explain what it returns.
- Write a query to find the second-highest salary in a table.
- What's the difference between a LEFT JOIN and an INNER JOIN? When would each change your result?
- How do you find duplicate rows, and how would you remove them?
- Explain GROUP BY vs. a window function. When is a window function the better tool?
- Write a query to get each customer's most recent order.
- What does a WHERE clause do that a HAVING clause can't, and vice versa?
- How would you calculate a running total or a month-over-month change in SQL?
Say your reasoning as you write. Interviewers care more about how you break down the problem than whether you nail the syntax on the first try.
What statistics and analysis questions come up?
These check whether you understand the numbers you produce, not just how to produce them.
- What's the difference between correlation and causation, and how do you avoid confusing them?
- When would you use the median instead of the mean?
- What is a p-value, in plain terms?
- How do you handle missing data in a dataset?
- What is selection bias, and how might it creep into an analysis?
- How would you tell whether a change in a metric is real or just noise?
How do you handle a business case or scenario question?
Case questions test judgment. There's rarely one right answer, so they watch how you structure the problem.
- Weekly active users dropped 15% last week. How do you investigate?
- The product team wants to know if a new feature is working. What would you measure?
- How would you decide which customer segment to target for a retention campaign?
- Sign-ups are up but revenue is flat. What's your first hypothesis?
Start by clarifying the question, then break it into pieces (segments, time windows, data sources), state your assumptions, and describe what you'd check first and why.
What tool questions should you expect (Excel, SQL, BI)?
Expect specifics about the tools listed in the job description.
- Excel: how do you use VLOOKUP or INDEX/MATCH, and when would you reach for a pivot table?
- SQL: which dialects have you used, and how do you optimize a slow query?
- BI tools: how do you build a dashboard in Tableau or Power BI that a non-technical stakeholder can actually read?
- How do you decide when a problem belongs in SQL versus a spreadsheet versus a BI tool?
What behavioral questions do data analysts get?
These check communication and how you work with stakeholders.
- Tell me about a time your analysis changed a decision.
- Describe a time you had to explain a technical finding to a non-technical audience.
- Tell me about a time your data was messy or incomplete. What did you do?
- Describe a time you disagreed with a stakeholder about what the data showed.
A worked STAR answer
Question: Tell me about a time your analysis changed a decision.
Situation: "Our support team wanted to hire two more agents because ticket volume looked high."
Task: "I was asked to confirm the volume before we opened the roles."
Action: "I pulled six months of tickets and broke them down by category. About 40% came from a single billing page where a confusing error message pushed people to contact support. I checked the timing and the spike lined up exactly with a release on that page."
Result: "I shared it with the product team, they fixed the error message, and ticket volume dropped by roughly a third within two weeks. We held off on one of the two hires and saved that budget."
Notice the answer leads with the business outcome, not the query. That's what interviewers want to hear.
What questions should you ask the interviewer?
- What does the data stack look like day to day (warehouse, BI tool, SQL dialect)?
- Who consumes the analysis, and how are decisions made from it?
- How clean and documented is the data I'd be working with?
- What would a successful first 90 days look like in this role?
How to prepare for a data analyst interview
Practice SQL by talking through queries out loud, not just writing them silently. Pick two or three real projects and rehearse them in STAR form, leading with the business impact. Read the job description closely and match your prep to the tools it names. For a broader list of prompts across roles, see these mock interview questions, and if you want to understand the format, this guide to mock interviews covers what to expect.
The fastest way to improve is to rehearse under real pressure. On Nova Interviewer you can paste the job description and practice these in a mock interview that grades you, then get coaching feedback on where your answers fell short. The first practice interview is free.
Bottom line: Data analyst interviews reward clear reasoning over memorized answers. Know your SQL, be able to defend your statistics, structure any business case out loud, and tell project stories that end in a decision or a dollar figure. Practice each part until you can explain it plainly.