Data Analyst Interview Questions UK: 8 Real Questions and What Interviewers Want

Before your Data Analyst interview, it helps to see the questions from the interviewer's side. We have picked eight from our set of 25 and added the reason each one gets asked.

Eight Data Analyst interview questions, and what the interviewer is thinking

1. Walk me through a typical data analysis project from request to delivery.

Why they ask: The most common opener; they want to see your end-to-end process.

2. What is the difference between INNER JOIN, LEFT JOIN and FULL OUTER JOIN, and how can joins cause duplicated rows?

Why they ask: SQL joins are tested in nearly every UK data analyst interview.

3. How would you use window functions in SQL? Give an example.

Why they ask: Window functions separate competent from basic SQL users.

4. How do you clean and prepare a messy dataset in Python using pandas?

Why they ask: Data cleaning is most of the job.

5. How do you handle missing data, and does it matter why it is missing?

Why they ask: Shows statistical judgement.

6. What Excel skills do you use as a data analyst, and when would you move away from Excel?

Why they ask: Excel remains ubiquitous in UK workplaces.

7. A stakeholder asks for a figure by the end of the day, but you know the data is incomplete. What do you do?

Why they ask: Integrity under time pressure.

8. Tell me about a time your analysis led to a change in a business decision.

Why they ask: Impact is what hiring managers buy.

A sample answer to question 1

Walk me through a typical data analysis project from request to delivery.

I start by clarifying the business question: who is asking, what decision it supports and what a useful answer looks like. Then I identify the data sources, check access and get a feel for quality through profiling, looking at row counts, nulls, duplicates and ranges. I clean and join the data, usually with SQL and then Python or Excel, and document every assumption. Next comes exploratory analysis to find patterns and test hypotheses, using appropriate statistics rather than just eyeballing charts. I then build the output, which might be a dashboard in Power BI or Tableau or a short written summary with two or three clear recommendations. Before sharing, I validate numbers against a trusted source and get a peer review. Finally I present, gather feedback and track whether the recommendation made a difference.

Common mistake to avoid: Jumping straight to the data; skipping validation

Want the rest?

Full model answers, key points and common mistakes for all 25 questions are on the Data Analyst interview questions page. The first five are free, no sign-up needed.