Revolut Data Analyst candidates commonly report recruiter screening, timed assessments or live SQL, then technical and fit conversations. Prepare practical SQL, metrics reasoning, and concise explanations of past analytical work.
$90K
Avg. Base Comp
$110K
Avg. Total Comp
3-6 rounds
Typical Rounds
Not reported
Process Length
Revolut Data Analyst interviews in the supplied accounts emphasize practical analytics and clear explanations of problem-solving. Candidates describe recruiter discussions, live SQL, broader technical questions, and conversations about projects or business scenarios. The exact end-to-end sequence is not established consistently, so use these reports as examples of the skills that may be assessed rather than as a fixed interview plan.
Live SQL is the clearest recurring theme. One candidate described a 60-minute session with four questions based on tables for users, transactions, events, and subscriptions. Reported tasks included monthly active users with completed transactions, users in the top 10% by transaction volume, joins, aggregations, CTEs, filtering, and date logic. Another candidate was told to expect medium-difficulty analytics SQL involving window functions, cohort or funnel questions, and conversion rates. Practice checking the grain of each table, stating assumptions, and explaining how you would validate the result.
Other exact-role accounts mention Python data manipulation, pandas, data cleaning, statistics, machine learning, and data-quality issues. Candidates were asked to discuss missing values, outliers, model selection, overfitting, data leakage, and evaluation metrics such as precision, recall, and F1. These topics appear in individual accounts rather than a single universal round.
Business framing accompanies the technical work. Reported discussions include customer transaction analysis, funnels, metrics, experimentation, and investigating conversion changes. Prepare a few concise project examples that cover the problem, your approach, important trade-offs, and business impact. The strongest evidence concerns technical questioning, while the later-stage format remains less clearly reported.
Synthesized from 27 candidate reports by our editorial team.
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Real interview reports from people who went through the Revolut process.
The interview process was structured and fairly straightforward. I felt most confident when discussing my background, motivation and previous projects, while the more technical and situational questions required more thought. The interviewers were professional and gave me enough time to explain my reasoning. Overall, the process felt challenging but reasonable, and the biggest surprise was how much emphasis they placed on practical examples and problem-solving rather than textbook answers.
Questions asked: I was asked a mix of SQL, Python, statistics and machine learning questions. The SQL questions covered joins, GROUP BY, WHERE vs HAVING, aggregations, NULL handling/COALESCE, CTEs and how I would approach analysing customer transaction data. For Python, there were questions around data structures, pandas, data cleaning and basic functions. Statistics included p-values, statistical significance, variance and interpreting results. Machine learning topics included supervised vs unsupervised learning, train/test split, overfitting, feature scaling, data leakage and evaluation metrics such as precision, recall and F1-score. I was also asked to explain a project where I had used SQL and Python to solve a real data problem and to walk through my reasoning rather than just give the final answer.
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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 | |
| Post Composer Drop | |
| Daily Logins | |
| Size of Joins | |
| Google Maps Improvement | |
| Declining Applicants | |
| Payments Received | |
| Subscription Retention | |
| Time on FB Distribution | |
| Sort Strings | |
| Retailer Data Warehouse | |
| Hurdles In Data Projects | |
| Success Measurement | |
| Cumulative Reset | |
| Word Frequency | |
| Assumptions of Linear Regression | |
| Testing Price Increase | |
| Duplicate Rows |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter or HR call in several routes. Discussions may cover prior experience, projects, motivation for Revolut, and relevant SQL, product-analytics, or stakeholder experience; one candidate reported a 30-minute screening.
Some candidates report HackerRank-style assessments before or around screening. Formats vary: reported examples include SQL and Python tasks, and another assessment combined SQL with statistics and A/B-testing questions. Time limits were reported, so candidates may need to prioritize clear, working logic.
Candidates frequently report a live SQL session with multiple questions on a shared schema. Reported work includes joins, aggregations, date functions, CTEs, window functions, rankings, rolling calculations, funnels, and transaction analysis, with explanation of reasoning expected during the exercise.
Candidates report SQL prompts that require defining a metric as well as questions about product success, KPI selection, fraud, approval declines, and experimentation. Expect to state assumptions, identify a decision-relevant metric, and explain how the analysis would be evaluated.
Later stages may include a data-skills interview, a discussion of previous projects, and a team-fit conversation. Candidates report being asked to walk through their approach, trade-offs, challenges, and impact rather than only provide a final technical answer.
