Revolut's Data Engineer process typically runs 4-5 rounds built around an online SQL/Python assessment and live SQL on a shared users/transactions/activity schema, followed by later Python, data-modeling, or hiring-manager and team stages.
$123K
Avg. Base Comp
$155K
Avg. Total Comp
4-5 rounds
Typical Rounds
Not reported
Process Length
Revolut's Data Engineer interviews lean heavily on live SQL, typically layered across a recruiter screen, an online coding assessment, and at least one live-coded SQL session built around a shared three-table schema of users, transactions, and an activity or events table. Candidates report that the live SQL round is timed around 45 minutes and runs through four questions on the same schema. Accounts mention a triple join, a ranking or top-N problem, a time-windowed cohort or derived-ratio metric, and harder scenarios such as flagging IP addresses that logged in from multiple locations or an open-ended business case like a fraud problem. Several candidates note the interviewer cares as much about how you reason through edge cases (nulls, duplicate records, inclusive date windows, divide-by-zero) as about a perfect query, and that narrating your approach before writing code matters.
Python shows up as well. It ranges from string-splitting, list-comprehension, and pandas filtering questions in the online assessment to a separate Python live-coding round that one candidate was told about during the HR call. The process can also include a take-home data-modeling exercise covering data quality, slowly changing dimensions, and dbt snapshots. One HR screener pushed into technical territory, asking for a walkthrough of a Python-and-SQL pipeline, how its Airflow DAG was structured, and how documentation and governance were handled. Candidates also mention a hiring-manager or team round near the end, though the reported accounts don't describe what is asked there. Reported round counts vary somewhat by candidate, so treat this structure as a typical pattern rather than a fixed script.
Synthesized from 9 candidate reports by our editorial team.
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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 | |
| Daily Logins | |
| Top 3 Users | |
| Third Purchase | |
| Retailer Data Warehouse | |
| Rolling Average Steps | |
| Total Spent on Products | |
| Find the First Non-Repeating Character in a String | |
| Daily Retention Summary | |
| Size of Joins | |
| Post Composer Drop | |
| Cumulative Reset | |
| Real-Time Transaction Streaming | |
| Google Maps Improvement | |
| Subscription Retention | |
| Sort Strings | |
| Hurdles In Data Projects | |
| Word Frequency | |
| Duplicate Rows | |
| Suspicious Card Activity | |
| Digitizing Student Test Scores | |
| Count Transactions | |
| Payments Received | |
| Time on FB Distribution |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial HR or recruiter conversation covering background, salary expectations, relocation, and the team's stack (one candidate heard Looker, BigQuery, and Trino). One candidate noted this screener included technical follow-ups, such as describing their own Python/SQL pipeline, how its Airflow DAG was structured, and how they handled documentation and governance.
Several candidates describe a timed online test (on platforms like CoderPad or HackerRank) combining SQL and Python questions. The SQL questions covered rolling or moving averages, grouping and ranking, and timestamp conversion. The Python questions covered string manipulation, list comprehensions, pandas filtering, and word-frequency style logic. Copy-paste is sometimes disabled, and output can display in a less familiar terminal format rather than a table.
Multiple candidates describe a live, interviewer-present SQL round, often on CoderPad with a shared three-table schema (users, transactions, and an activity/events table) and roughly a 45-minute limit. Reports describe four questions on the same schema, including a triple join, a ranking or top-N problem, a time-windowed cohort or derived-ratio metric, and harder scenarios like flagging suspicious IP logins or an open-ended business case. Candidates say interviewers weigh reasoning about edge cases as much as correct syntax.
One candidate was told during the HR call that the process includes a Python live-coding round after the live SQL round. The reported accounts don't describe the specific questions in this round, so expect it to build on the Python fundamentals tested in the online assessment.
One account describes a take-home ETL design exercise covering data quality, slowly changing dimensions, and dbt snapshots. Another candidate's process included two technical interviews mixing SQL and problem-solving, where interviewers probed window functions, including why to choose ROW_NUMBER() versus RANK(), as well as running totals and edge cases like users with no transactions.
One candidate's five-round process ended with a team interview, and another was told the final step would be a hiring manager round. The reported accounts don't describe the questions or outcome of this stage.
