Reported Revolut Data Analyst interviews include screening conversations, timed SQL work, and data-skills or fit discussions. Focus on practical query reasoning, clear communication, and metrics judgment.
$70K
Avg. Base Comp
$140K
Avg. Total Comp
3-6 rounds
Typical Rounds
Not reported
Process Length
Revolut Data Analyst candidates most consistently describe practical SQL evaluation alongside conversations about their experience and analytical judgment. SQL is the strongest recurring theme. Reports include live coding and assessment formats using transaction, user, event, subscription, or payment-style data. Common tasks involve joins, aggregations, date logic, ranking, window functions, rolling calculations, and month-over-month comparisons.
The technical format varies. One candidate reported a 30-minute recruiter call followed by a one-hour live exercise with two SQL questions and two business-concept questions. Another described a 45-minute shared-editor SQL interview without query execution, followed by a 45-minute data-skills interview and a team-fit call. Other reports describe HackerRank-style SQL and statistics assessments, while one candidate encountered live Python and SQL interviews later in the process.
Prepare to explain your query before relying on a result. State the data grain, identify joins and duplicate risks, and talk through edge cases. Candidates also report questions about defining metrics, interpreting whether a product performed well, identifying potentially fraudulent transactions, and discussing prior projects or motivation. Bring concise examples of your work and connect technical choices to the decisions they support.
One candidate described a three-round process, while other reports outline particular stages without establishing a consistent overall round count. The exact sequence appears to vary by role and candidate.
Synthesized from 19 candidate reports by our editorial team.
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| 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 | |
| Post Composer Drop | |
| Size of Joins | |
| Daily Retention Summary | |
| Daily Logins | |
| Google Maps Improvement | |
| Declining Applicants | |
| Payments Received | |
| Subscription Retention | |
| Time on FB Distribution | |
| Sort Strings | |
| Hurdles In Data Projects | |
| Success Measurement | |
| Word Frequency | |
| Cumulative Reset | |
| Assumptions of Linear Regression | |
| Testing Price Increase | |
| Duplicate Rows | |
| Count Transactions |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report recruiter or HR conversations covering background, prior projects, motivation for joining Revolut, and relevant analytical experience. One account describes a 30-minute recruiter call before technical work; another reports a short HR screening after an assessment. Prepare a concise account of your experience and the analytical decisions you have made.
Reported technical formats include HackerRank-style assessments and live SQL sessions. One candidate described a 45-minute shared-editor interview with no query execution, while another reported a one-hour live exercise. Questions commonly use multi-table analytical data and test joins, aggregations, date functions, window functions, ranking, rolling calculations, and business-oriented query logic.
Some candidates report a data-skills interview and a team-fit call after live coding; other accounts mention manager, product, or behavioral conversations. Reported prompts include explaining prior work, discussing a project challenge, defining a metric for a new plan, and reasoning about whether a product performed well. Explain assumptions, validation steps, and trade-offs clearly.