Revolut Data Analyst candidates report a recruiter screen, an online or live SQL assessment, and product-analytics or team-fit conversations. Expect transaction-table SQL, window functions, metrics, and A/B testing.
$100K
Avg. Base Comp
$115K
Avg. Total Comp
3-6 rounds
Typical Rounds
Not reported
Process Length
Candidates describe the Revolut Data Analyst interview as SQL-heavy and time-pressured, built around transaction and user data. The most common reported pattern is a recruiter or HR call, an online SQL assessment, and a live SQL coding interview. Reported round counts range from three to six, depending on the candidate.
The SQL questions are fintech-flavored. Candidates report these examples:
Window functions such as LAG, ROW_NUMBER and RANK come up repeatedly. One candidate was asked why ROW_NUMBER() was chosen over RANK(), and how they would validate the results. Be ready to justify your choices and to handle edge cases such as users with no previous month of activity.
Several candidates say interviewers care more about logical thinking than perfect syntax, and that the live format adds pressure. Practice talking through your approach calmly while you write.
Product thinking appears alongside SQL. Reports mention assessing the performance of a newly released plan with no single defined metric, investigating drops in conversion, and funnel or cohort questions. One online assessment combined SQL with statistics, including minimum detectable effect, sample size and bias-variance. Some candidates also mention Python.
Synthesized from 43 candidate reports by our editorial team.
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Real interview reports from people who went through the Revolut process.
The question I kept thinking about afterward came in the technical deep-dive: how would you investigate a sudden drop in payment authorization rates for cross-border debit transactions, and what trade-offs would you weigh before implementing a fix? I got an offer, so here is how the whole process went.
The first stage was a recruiter screen. It was an introductory call covering my background, my experience with data and fintech, culture fit, and salary expectations. Communication from the People and Talent Acquisition team was prompt and clear from that point on.
Next was a timed SQL and analytical assessment. The SQL covered window functions, aggregations, joins, and fairly complex CTEs, all set against financial datasets rather than generic tables. The analytical portion asked me to work through a business problem in the same domain. If you're preparing, practice with transaction-style data where you have to think carefully about what each row represents, because that shapes the queries you write.
The technical deep-dive was with a Senior Data Lead and involved live work. We reviewed technical scenarios, queried data structures, and talked through practical metrics such as authorization rates, transaction routing, and unit economics. The authorization rate question came up here. It tests whether you can isolate where a metric breaks before reaching for a fix, and whether you can reason about the trade-offs of a change in a payments system, where a routing adjustment can help one segment and hurt another.
The final round was a behavioral and business fit session with a bar raiser. It focused on Revolut's core values, how I approach problems, product intuition, and how I prioritize deliverables in a fast-paced environment. Expect questions that ask you to justify decisions, not just describe what you did.
Overall, the process felt structured, transparent, and rigorous. The technical rounds were grounded in the metrics the team works with daily, so knowing those metrics well mattered more than memorizing syntax.
Prep tip from this candidate
Practice diagnosing a sudden drop in a payment metric by isolating the segment first, then talk through the trade-offs of a fix out loud, since the authorization-rate question tests that reasoning directly. Also drill SQL on transaction-style data with window functions and multi-step CTEs.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Revolut
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| Rolling Bank Transactions | |
| Top Three Salaries | |
| Experiment Validity | |
| Last Transaction | |
| Like Tracker | |
| Month Over Month | |
| Button AB Test | |
| Top 3 Users | |
| Third Purchase | |
| Rolling Average Steps | |
| Total Spent on Products | |
| Daily Retention Summary | |
| Size of Joins | |
| Post Composer Drop | |
| Daily Logins | |
| Google Maps Improvement | |
| Declining Applicants | |
| Payments Received | |
| Subscription Retention | |
| Retailer Data Warehouse | |
| Time on FB Distribution | |
| Sort Strings | |
| Hurdles In Data Projects | |
| Success Measurement | |
| Cumulative Reset | |
| Assumptions of Linear Regression | |
| Word Frequency | |
| Testing Price Increase | |
| Duplicate Rows |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report a short recruiter or HR call covering background, SQL and product analytics experience, motivation for Revolut, and sometimes salary expectations. One candidate says the recruiter stressed pace and ownership, so prepare a concise project story and a clear reason for applying.
Several candidates report a timed HackerRank test, though not everyone describes one. Reports mention 60 to 75 minutes with SQL plus either Python or statistics and A/B testing questions. One candidate found it really difficult under the time limit, so time management matters.
Candidates report four SQL questions in about 60 minutes, often on a multi-table schema of users and transactions. Topics include joins, window functions, monthly active users, rolling averages and top-N queries. Interviewers reportedly focus on logical thinking over syntax, so explain your reasoning aloud and handle edge cases.
Some candidates describe further interviews after the SQL round. These include a manager or product conversation, a skills discussion, or a second live coding round. Reported product topics include metrics, funnels, growth and investigating conversion drops. Clear, calm reasoning reportedly matters more than polished answers.
One candidate mentions a final team-fit round after a skills interview. Hiring timelines are not clearly reported. Prepare examples of ownership, trade-offs and the impact of your work.
