- Stages
- 5
- Typical timeline
- 2 to 5 weeks
- Difficulty
- Medium
- Format
- SQL screen, case or take-home, stakeholder interview
Data analyst interviews test SQL, analytical thinking and communication. Expect a live SQL screen with joins, aggregation and window functions, questions on statistics such as averages, distributions and A/B test results, and a case or take-home where you analyse a dataset and present a recommendation. Interviewers care about whether you check data before trusting it, choose the right metric and explain findings simply to people who are not analysts. The strongest candidates finish every analysis with a clear recommendation and say how confident they are in it.
How Wise hires
Wise interviews typically include a recruiter screen, role-specific interviews and conversations about the company's mission of making money move across borders cheaply and quickly.
The interview process, stage by stage
- Recruiter screenTools, past analyses and motivation.
- SQL screenLive queries on a sample schema.
- Case or take-homeAnalyse a dataset and recommend a decision.
- Stakeholder interviewExplain results to a non-technical audience.
- Final interviewTeam fit, behavioural questions and values.
How you're scored
Correct SQL and sound analysis.
Breaks problems down and checks assumptions.
Chooses metrics that matter and makes a recommendation.
Explains findings clearly and simply.
Likely Data Analyst interview questions
Write a query that returns each user's first purchase date and whether they bought again within 30 days.
A strong answer includes: Find the first purchase with a window function or MIN, join back to later purchases within 30 days, handle users with one purchase and state time zone assumptions.
An A/B test shows a 2% lift that isn't statistically significant. What do you recommend?
A strong answer includes: Check sample size and duration, look for novelty effects and segment issues, then recommend extending, re-running or shipping based on cost and risk, with reasons.
A key metric doubled overnight. What do you check?
A strong answer includes: Tracking changes, duplicated data, pipeline issues and definition changes first, then real causes such as marketing campaigns, seasonality or bots.
Every question for this interview, with what strong answers include. Free with an account for the first few; all of them with Pro.
Common mistakes
- Not checking for duplicates or missing data
- Describing data without a recommendation
- Ignoring sample size in test results
- Dashboards full of charts with no insight
Questions to ask them
- Who are the main stakeholders for this team's analysis?
- What tools and data warehouse do you use?
- How are A/B tests designed and reviewed?
- What analysis would you want done in my first month?
Candidate experiences
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Share your interviewFrequently asked questions
What is the Wise Data Analyst interview process?
Wise interviews typically include a recruiter screen, role-specific interviews and conversations about the company's mission of making money move across borders cheaply and quickly. For Data Analyst roles, interviews usually cover recruiter screen, sql screen, case or take-home, stakeholder interview, final interview. Timelines are typically 2 to 5 weeks, but they vary by team and level.
How do I prepare for a Data Analyst interview at Wise?
Learn how Wise works and who its customers are, then prepare for the core Data Analyst rounds: sql, statistics, data visualisation. Practise the likely questions out loud, prepare specific stories with results, and have thoughtful questions ready for your interviewers.
What is asked in a data analyst interview?
SQL queries with joins, grouping and window functions, basic statistics, interpreting A/B tests, a case study or take-home analysis, and questions about how you explain findings to stakeholders. Some roles also test Excel or a visualisation tool.
How do I prepare for a data analyst SQL test?
Practise joins, aggregation with GROUP BY and HAVING, window functions such as ROW_NUMBER and LAG, date handling and deduplication. Talk through the result you expect before running a query and check edge cases like nulls.
