Uber Data Analyst interviews typically include a recruiter screen, a hiring-manager conversation, and SQL-heavy technical rounds, with some loops adding a business case or take-home analysis; two candidates reported processes of roughly two months.
$133K
Avg. Base Comp
$155K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Uber's Data Analyst interviews typically open with a recruiter or HR screen covering background and motivation for joining Uber, and in at least one case salary expectations, before moving into a hiring-manager conversation and one or more technical rounds. SQL is the clearest recurring theme: candidates report questions on joins, window functions (RANK, LEAD, LAG), GROUP BY/HAVING filtering, and writing queries from given tables and expected output. Several candidates also describe product and business case discussions, including rider booking funnels, fraud-related ride cancellations, A/B test design for clearer fare and ETA messaging, and root-causing a metric drop after an experiment. One process added a take-home analysis that had to be turned into a presentation and walked through with the hiring manager. In a separate interview, a leadership interviewer pressed the candidate on how they had sourced their data and why they had not included other data points. One longer loop added PM and director rounds focused on measuring the success of a feature or fraud-prevention launch.
Length and format vary by candidate. Two candidates described processes of roughly two months, and one of them received a late eligibility-policy rejection after the interviews were already complete. Another faced a timed 60-minute assessment mixing aptitude questions with SQL before two shorter interview rounds. Given this spread, prepare SQL fundamentals thoroughly, practice structuring an open-ended business case out loud, and be ready to defend your analytical choices. Confirm eligibility requirements early rather than assuming a quick, uniform timeline.
Synthesized from 13 candidate reports by our editorial team.
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Featured question at Uber
Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.
| Question | |
|---|---|
| Experiment Validity | |
| Download Facts | |
| User Experience Percentage | |
| Weighted Keys | |
| Button AB Test | |
| Top 3 Users | |
| Third Purchase | |
| Maximum Profit | |
| Bank Fraud Model | |
| Encoding Categorical Features | |
| Network Experiment Design | |
| Distance Traveled | |
| Revenue Retention | |
| Size of Joins | |
| P-value to a Layman | |
| Christmas Dinner Ingredient Optimization | |
| Google Maps Improvement | |
| Random Forest Explanation | |
| Type-ahead Search | |
| WAU vs Open Rates | |
| Sort Strings | |
| Uber User Journey | |
| Binary Tree Conversion | |
| Hurdles In Data Projects | |
| Xgboost vs Random Forest | |
| Bloated Mid-Funnel | |
| Production Model Monitoring | |
| Dijkstra implementation | |
| Uniform Car Maker |
Synthesized from candidate reports. Individual experiences may vary.
Most candidates report starting with a recruiter or HR screen covering background and motivation for joining Uber. One candidate's HR screen over Google Meet was mostly behavioral, covering problem-solving, teamwork, salary expectations, and an example of using data to solve a client challenge. Others described this stage as straightforward and friendly, focused on fit before the loop moves into technical rounds.
Candidates report a hiring-manager round blending behavioral questions with discussion of past work. One candidate's first hiring-manager round spent about 15 minutes on their CV before two or three SQL questions and a short fraud ride-cancellation case. Another walked the hiring manager through a take-home case study and had to explain the choices behind it. One offer recipient described questions on rider behavior, funnel analysis, experimentation, and working with product teams.
SQL appears in nearly every reported loop, ranging from grouping and filtering with GROUP BY and HAVING to joins and window functions like RANK, LEAD, and LAG. One candidate was given tables with expected input/output and asked to write queries in a round that also covered pandas. Another faced a 60-minute assessment with 15 aptitude questions, two medium SQL problems, and a few basic ML questions before the live interviews. One remembered question asked for the client who spent the most in the previous quarter from a transaction table.
A recurring stage asks candidates to reason through an open-ended scenario, such as a drop in rider booking conversion, fraud-related ride cancellations, or designing an A/B test for clearer fare and ETA messaging. One candidate prepared a take-home analysis and presentation for the hiring manager. Another described a tense exchange with a leadership interviewer who questioned how they had sourced their data and why they had not included other data points.
In one longer loop, a virtual onsite included a PM round on root-causing a metric drop after an experiment launch and a director round on measuring success for a feature or fraud-prevention launch. Other candidates described a team-leader conversation or an HR round as the final step, so expect this stage to vary in length and format rather than follow one fixed structure.
