Uber Big Data Engineer Interview Questions (2026)
The 15 Big Data Engineer interview questions most worth practising for Uber, selected from a bank of 72. Build distributed pipelines that ingest, process, and store terabytes of data reliably at scale. Below: the interview process, the questions with answer outlines, the topics tested, and how to prepare.
Uber runs a fast, bar-heavy loop: a CodeSignal or live coding screen, then a virtual onsite with two coding rounds, a system design round steeped in real-time/marketplace problems, and a behavioral round mapped to its rewritten cultural norms. Uber India (Bangalore/Hyderabad) engineering interviews at the same global bar.
Questions
15
from a 72-question bank
Difficulty
Medium
from our question mix
Rounds
6
typical loop
Uber rating
4.02/5
Top 99% in Internet
Uber's interview process
- 1Recruiter Screen30 minEasy
Role targeting, level calibration and process expectations.
- 2Technical Phone Screen60 minMedium
One or two medium DSA problems (CodeSignal or live) with emphasis on correct, runnable code and edge cases.
- 3Onsite Coding I60 minHard
Practical problem such as building a rate limiter or an in-memory index, judged on working code and API cleanliness.
- 4Onsite Coding II60 minHard
Algorithmic problem often with a geospatial or streaming flavor, pushed to optimal complexity.
- 5System Design60 minHard
Design a real-time marketplace system (dispatch, ETA, surge) with hard follow-ups on scale, geo-sharding and failure modes.
- 6Behavioral / Hiring Manager Round45 minMedium
STAR stories mapped to Uber's cultural norms: ownership, bold bets, customer obsession and conflict handling.
Big Data Engineer interview questions for the Uber loop
- Q1
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 ResolutionHow to answer:State both positions fairly, explain evidence gathered, describe the decision process, and show the relationship stayed healthy.
- Q2
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 DesignHow to answer:Measure cost by owner and workload, optimize scans and files, right-size compute, cache or materialize common aggregates, and enforce budgets.
- Q3
Design observability for Uber's critical trips pipelines across freshness, quality, volume, and cost
MediumRound 6: System DesignData ObservabilityHow to answer:Collect SLIs for freshness, completeness, validity, failure rate, latency, and spend; alert on symptoms and attach run-level lineage.
- Q4
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 QualityHow to answer:Check schema, nullability, uniqueness, referential integrity, volume anomalies, value ranges, freshness, and reconciliation against source totals.
- Q5
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 DesignHow to answer:Use durable event ingestion, streaming processing, curated storage, low-latency serving, monitoring, and replayable raw logs.
- Q6
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 ArchitectureHow to answer:Land raw data, validate schema, transform to curated tables, run data quality checks, publish aggregates, and monitor freshness and failures.
- Q7
How would you make Uber's Airflow DAG for trips processing idempotent and safe to backfill?
HardRound 5: ETL DesignETL OrchestrationHow to answer:Use deterministic input ranges, write to temporary paths, validate outputs, atomic swap/merge, and parameterize DAG runs by logical date.
- Q8
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 PrivacyHow to answer:Describe the concern, policy or risk, who you involved, the decision, and how the safer approach still met business needs.
- Q9
For Uber, would you model trip, driver-location, and pricing events as an event fact table, an accumulating snapshot, or a periodic snapshot? Defend the choice
HardRound 4: Data ModelingFact Table DesignHow to answer:Use event facts for immutable actions, accumulating snapshots for lifecycle progress, and periodic snapshots for state at regular intervals.
- Q10
Design access control for Uber's analytics platform where precise rider location must be protected but aggregated analysis is allowed
HardRound 6: System DesignGovernance and SecurityHow to answer:Use role-based and attribute-based controls, row/column masking, tokenization, governed joins, audit logs, and least-privilege access.
- Q11
For Uber's trips data, design an incremental load using CDC or high-watermark logic
HardRound 5: ETL DesignIncremental ETLHow to answer:Capture changes since the last checkpoint, deduplicate, handle deletes and updates, merge into curated tables, and persist checkpoints transactionally.
- Q12
How would you add lineage and auditability to Uber's trips ETL pipeline?
MediumRound 5: ETL DesignLineage and AuditHow to answer:Capture source version, run ID, code version, input/output counts, checksums, timestamps, and upstream/downstream table dependencies.
- Q13
In Uber's ride-sharing marketplace, design a bridge table for many-to-many relationships between riders and drivers
HardRound 4: Data ModelingMany-to-Many ModelingHow to answer:Create a bridge table with surrogate relationship keys, effective dates when needed, allocation weights if measures must be split, and referential integrity checks.
- Q14
Why Uber, and how does your experience map to ownership, speed, marketplace thinking, and rider/driver empathy?
EasyRound 7: Hiring ManagerMotivation and FitHow to answer:Connect specific company problems to your past work, show motivation, and give examples that demonstrate the stated values.
- Q15
Choose partitioning and clustering keys for Uber's fact_trips to support common queries by time and city
MediumRound 4: Data ModelingPhysical Data ModelingHow to answer:Partition primarily by event date, cluster or sort by city and high-value join/filter keys, and avoid high-cardinality partitions.
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Topics tested most
How to prepare for the Uber Big Data Engineer interview
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).
Frequently asked questions
How hard is the Uber Big Data Engineer interview?
Based on our 72-question Big Data Engineer bank for the Uber loop, the overall difficulty is medium (Uber's process is generally rated elevated). Expect around 6 rounds spanning Accountability, Ambiguity, Conflict Resolution.
How many interview rounds does Uber have for a Big Data Engineer?
Uber typically runs about 6 rounds for Big Data Engineer candidates: Recruiter Screen → Technical Phone Screen → Onsite Coding I → Onsite Coding II → System Design.
What is the interview process at Uber?
The Uber interview process typically runs: Recruiter screen -> technical screen -> onsite (coding x2, system design, behavioral). Prepare for each round in order rather than only the first — the later stages usually carry the most weight.
How hard is the Uber interview?
Uber interviews are rated high difficulty. The bar is highest on coding — go deep there and practise explaining your reasoning out loud.
What does Uber look for in candidates?
Uber focuses on Coding, large-scale system design, analytical thinking. Culturally, it values We build globally, customer obsession, bold bets, ownership. Line up your examples to hit both the technical bar and these values.
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Compiled by PrepNPlaced from 72+ interview reports and question banks for the Uber Big Data Engineer loop, cross-referenced with 1,075 employee reviews. Data refreshed 2026-08-13. Updated 2026.