Revolut Data Analyst Interview Guide

We've seen Revolut lean hard on practical SQL fluency with real business logic, not toy syntax. Multiple candidates reported questions built around users, transactions, events, and subscriptions, with prompts like monthly active users with at least one completed transaction and identifying the top 10% of users by transaction volume. That combination tells us the team cares less about memorized patterns and more about whether you can translate a product metric into clean relational logic under pressure.

A recurring theme is the mix of straightforward aggregation and more demanding edge cases. Our candidates report rolling averages, top salary by department, and joins across five tables, which suggests they want analysts who are comfortable moving from basic grouping into window functions and multi-table reasoning without losing accuracy. One candidate noted that some problems felt “high standard,” and that tracks with the overall pattern: the bar is not just getting an answer, but getting the right answer in a schema that resembles a live fintech dataset.

We also see a meaningful emphasis on statistical judgment. Candidates mentioned A/B testing concepts like MDE, sample size, and bias-variance tradeoffs alongside distribution questions. That points to a company that expects analysts to explain why a metric moves, not just compute it. In our view, the non-obvious make-or-break here is being able to connect SQL output to decision-making, especially when the question blends transaction behavior with experimentation or product performance.