Uber Data Engineer Interview Questions (2026)

1. 1Recruiter Screen30 minEasy

   Role targeting, level calibration and process expectations.

2. 2Technical Phone Screen60 minMedium

   One or two medium DSA problems (CodeSignal or live) with emphasis on correct, runnable code and edge cases.

3. 3Onsite Coding I60 minHard

   Practical problem such as building a rate limiter or an in-memory index, judged on working code and API cleanliness.

4. 4Onsite Coding II60 minHard

   Algorithmic problem often with a geospatial or streaming flavor, pushed to optimal complexity.

5. 5System Design60 minHard

   Design a real-time marketplace system (dispatch, ETA, surge) with hard follow-ups on scale, geo-sharding and failure modes.

6. 6Behavioral / Hiring Manager Round45 minMedium

   STAR stories mapped to Uber's cultural norms: ownership, bold bets, customer obsession and conflict handling.

1. Q1

   For a Uber-like data project, tell me about a time you missed a deadline. What did you do?

   MediumRound 8: BehavioralAccountability

   How to answer: Communicate early, reset scope or timeline, explain root cause, protect critical users, and improve planning afterward.

2. Q2

   Give an example of ambiguous requirements for a data product similar to Uber's dashboards and downstream ML features. How did you clarify them?

   MediumRound 8: BehavioralAmbiguity

   How to answer: Identify users, decisions, metric definitions, freshness needs, edge cases, and acceptance criteria before building.

3. Q3

   At Uber, data decisions often involve trade-offs. Tell me about a conflict with another engineer over mobility data lakehouse or Kafka, Spark, Hive/Presto, and Airflow

   MediumRound 8: BehavioralConflict Resolution

   How to answer: State both positions fairly, explain evidence gathered, describe the decision process, and show the relationship stayed healthy.

4. Q4

   Design cost controls for Uber's mobility data lakehouse where query and pipeline spend is growing faster than usage

   MediumRound 6: System DesignCost and Performance Design

   How to answer: Measure cost by owner and workload, optimize scans and files, right-size compute, cache or materialize common aggregates, and enforce budgets.

5. Q5

   Design an event contract and schema registry process for Uber's mobile trip event stream producers and data consumers

   MediumRound 6: System DesignData Contracts

   How to answer: Define versioned schemas, compatibility rules, ownership, validation at ingestion, documentation, and a migration process for breaking changes.

6. Q6

   Model Uber's trip analytics using facts and dimensions. What is the fact grain?

   MediumRound 2: Trip Lifecycle Data ModelingData Modeling

   How to answer: The fact grain should be one row per business-defined trip unless a lifecycle-event grain is explicitly needed. Dimensions such as city, vehicle product, and user attributes should attach through stable keys. The explanation should emphasize declaring grain first, because metric correctness and join safety depend on it.

7. Q7

   How would you model app open, ride request, driver dispatch, and pickup events for Uber?

   MediumRound 2: Trip Lifecycle Data ModelingData Modeling

   How to answer: Keep a raw immutable event table, create typed staging models per event family, and build a lifecycle or funnel fact for analysis. The raw layer supports audit and replay, while analytics models standardize timestamps, identities, and event names. Good answers address schema evolution and duplicate events.

8. Q8

   vehicle product attributes change over time at Uber. Design the dimension table

   HardRound 2: Trip Lifecycle Data ModelingData Modeling

   How to answer: Use an SCD Type 2 dimension with surrogate key, natural key, effective_start, effective_end, is_current, and tracked attributes. Facts should join to the dimension version valid at the fact timestamp. This allows historical analysis without overwriting old attributes when current-state source values change.

9. Q9

   Uber needs to analyze trips by multiple classifications of vehicle product. When would you use a bridge table?

   HardRound 2: Trip Lifecycle Data ModelingData Modeling

   How to answer: Use a bridge table when one trip or dimension member can belong to multiple categories and a simple foreign key would force duplication. The bridge should include allocation weights if metrics need to sum correctly across classifications. The explanation should warn about double counting and clearly define how rollups are handled.

10. Q10

    Which conformed dimensions would help Uber compare completed trips, driver cancellation rate, and gross bookings across teams?

    MediumRound 2: Trip Lifecycle Data ModelingData Modeling

    How to answer: Common dimensions such as date, city, vehicle product, and user or account segments should be standardized across marts. Conformed dimensions reduce reconciliation debates because teams slice metrics the same way. The answer should mention ownership, slowly changing attributes, and backward compatibility.

11. Q11

    When would you use a periodic snapshot fact for Uber's trip lifecycle fact?

    HardRound 2: Trip Lifecycle Data ModelingData Modeling

    How to answer: Use a periodic snapshot when the business needs state at regular intervals, such as daily availability, balance, utilization, or inventory. It complements event facts by making point-in-time reporting easier. The explanation should include snapshot grain, date spine, storage tradeoffs, and handling corrections.

12. Q12

    Design an accumulating snapshot for the lifecycle from driver dispatch to pickup to trip completion at Uber

    HardRound 2: Trip Lifecycle Data ModelingData Modeling

    How to answer: Create one row per trip with milestone timestamps and statuses that update as the lifecycle progresses. This makes duration and bottleneck metrics easy to calculate. The answer should discuss mutable records, idempotent updates, late-arriving milestones, and whether completed records become immutable.

13. Q13

    Uber has anonymous events and logged-in events for riders. How would you model identity resolution?

    SeniorRound 2: Trip Lifecycle Data ModelingData Modeling

    How to answer: Maintain an identity graph that maps anonymous IDs, device IDs, and user IDs with validity windows and confidence levels. Analytics models should choose the identity appropriate to the metric, such as session-level or account-level. The explanation should address privacy, merge/split corrections, and reproducibility of historical metrics.

14. Q14

    How would you model adjustments such as fare adjustment or driver payout correction so Uber's gross bookings remains reconcilable?

    HardRound 2: Trip Lifecycle Data ModelingData Modeling

    How to answer: Use an append-only ledger or adjustment fact with transaction type, effective date, posted date, amount, currency, and source reference. Avoid overwriting historical amounts without an audit trail. The explanation should separate operational correction time from business effective time and show how net metrics are derived.

15. Q15

    Give a factless fact example for Uber involving app open or eligibility

    MediumRound 2: Trip Lifecycle Data ModelingData Modeling

    How to answer: A factless fact can record that a user, account, or item was eligible for an experience, policy, or inventory state even when no measurable transaction occurred. It supports denominator calculations and coverage analysis. The explanation should mention that absence of activity is meaningful only when the eligible population is modeled.

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Strong DSA and scalable system design; prepare analytical/behavioral stories

Indicative Data Engineer pay in India: ~₹10–45 LPA (role-level range, not a Uber-specific figure).

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