Uber Big 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

   Design observability for Uber's critical trips pipelines across freshness, quality, volume, and cost

   MediumRound 6: System DesignData Observability

   How to answer: Collect SLIs for freshness, completeness, validity, failure rate, latency, and spend; alert on symptoms and attach run-level lineage.

7. Q7

   Define data quality checks for Uber's trip_id, rider_id, driver_id, city_id, status, fare, event_time pipeline before publishing to analysts

   MediumRound 5: ETL DesignData Quality

   How to answer: Check schema, nullability, uniqueness, referential integrity, volume anomalies, value ranges, freshness, and reconciliation against source totals.

8. Q8

   Design a Uber data system for real-time surge-pricing and trip analytics with end-to-end latency of under 5 minutes

   HardRound 6: System DesignData System Design

   How to answer: Use durable event ingestion, streaming processing, curated storage, low-latency serving, monitoring, and replayable raw logs.

9. Q9

   Design a star schema for Uber's ride-sharing marketplace that supports reporting on completed trips, gross bookings, and request -> match -> pickup -> completion

   MediumRound 4: Data ModelingDimensional Modeling

   How to answer: Define a clear fact grain for trips, add conformed dimensions such as city, rider, and driver dimensions, and store additive measures separately from derived metrics.

10. Q10

    Design a daily ETL pipeline for Uber that ingests mobile trip event stream into mobility data lakehouse for completed trips reporting

    MediumRound 5: ETL DesignETL Architecture

    How to answer: Land raw data, validate schema, transform to curated tables, run data quality checks, publish aggregates, and monitor freshness and failures.

11. Q11

    A Uber ETL job failed halfway after writing partial data. Walk through the recovery design

    HardRound 5: ETL DesignETL Failure Recovery

    How to answer: Detect partial output, roll back or overwrite the affected partition, rerun from checkpoint, validate row counts, and publish only after atomic completion.

12. Q12

    How would you make Uber's Airflow DAG for trips processing idempotent and safe to backfill?

    HardRound 5: ETL DesignETL Orchestration

    How to answer: Use deterministic input ranges, write to temporary paths, validate outputs, atomic swap/merge, and parameterize DAG runs by logical date.

13. Q13

    Design reconciliation logic for Uber's ETL so gross bookings in mobility data lakehouse matches the operational source

    MediumRound 5: ETL DesignETL Reconciliation

    How to answer: Compare control totals by date and city, track accepted tolerances, investigate deltas, and block publishing on material mismatches.

14. Q14

    For Uber, decide between ETL and ELT for transforming trip, driver-location, and pricing events. What factors drive your choice?

    MediumRound 5: ETL DesignETL vs ELT

    How to answer: Choose ELT when the warehouse/lakehouse can scale transformations cheaply; choose ETL when privacy, bandwidth, or source constraints require pre-load shaping.

15. Q15

    For Uber's privacy-sensitive data such as precise rider location, tell me about a time you raised an ethics, privacy, or governance concern

    HardRound 8: BehavioralEthics and Privacy

    How to answer: Describe the concern, policy or risk, who you involved, the decision, and how the safer approach still met business needs.

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

Indicative Big Data Engineer pay in India: ~₹8–35 LPA (role-level range, not a Uber-specific figure).

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