Uber Senior Data Engineer Interview (L5)
Uber's Senior Data Engineer loop ((L5) short) emphasizes Marketplace and real-time systems focus with operator-style pragmatism. Candidates who clear it demonstrate independent technical leadership and cross-team influence backed by roughly 5-8 years.
Compensation
$195K–$245K base • $360K–$500K total (L5)
Loop duration
4 hours onsite
Rounds
5 rounds
Location
San Francisco, NYC, Sunnyvale, Seattle, Chicago
Compensation
Uber Senior Data Engineer total comp
Offer-report aggregate, 2026. Level mapped: L5. Typical experience: 8-10 years (median 9).
25th percentile
$276K
Median total comp
$368K
75th percentile
$441K
Median base salary
$212K
Median annual equity
$128K
Practice problems
Uber senior data engineer practice set
Uber senior data engineer practice set, mapped from predicted domain emphasis. Tap into any problem to work it in the live environment.
Binary Flag Indicators
The feature flag dashboard needs a clean boolean representation for downstream consumers. For each flag, show the flag name, a 1/0 indicator for whether it is enabled, and a 1/0 indicator for whether it is disabled.
The Overlap
Your monitoring system logs server maintenance as `[start, end]` minute ranges, and windows that overlap or sit back-to-back really describe one continuous outage. Collapse the `windows` so any that overlap or touch at an endpoint become a single range, and return them ordered by start time. Two windows touch when one ends exactly where the next begins.
Loose Ends
Every machine in our fleet writes one log line when a process starts and another when it stops, each line carrying the machine, a process id the machine numbers locally, the kind of event, and a timestamp in float seconds. Data scientists need the average elapsed time per process and timelines showing every process on a machine in order, so each start has to be reconciled with its matching stop. Design the data model for this log and describe how you would load the daily files through an ETL.
The Queue That Wouldn't Stop Growing
Your streaming video event pipeline shows consumer lag spiking from near-zero to over 500,000 messages within two hours. You need to diagnose whether the cause is a producer burst or a consumer slowdown, then design a monitoring and auto-remediation system that can detect, alert on, and automatically recover from future lag events.
Walk into Uber knowing the system design pattern they'll test.
Rolling 7-day active users
Count distinct users active in the trailing 7 days for each date. Product analytics staple.
Traders, Risk, and the Regulators
Markets move in milliseconds. The pipeline has to keep up.
Pulled from debriefs where system design separated levels.
The loop
How the interview actually runs
01Recruiter screen
30 minStandard screen with emphasis on operational mindset. Uber recruiters probe for pragmatism over theoretical elegance.
- →Emphasize operational wins: on-call reduction, SLA achievement, cost savings
- →Uber has many DE tracks: Rides, Eats, Freight, Maps, ML Platform, know the target
- →Ask about geographic focus, some teams are city-specific, some global
02Technical phone screen
60 minSQL with marketplace and geospatial flavor. Expect problems on trip matching, driver-rider supply/demand, and time-of-day patterns.
- →Practice geospatial SQL basics (H3 hexagons, city boundaries)
- →Time-bucketed analysis is ubiquitous: 15-minute windows, rush-hour detection
- →Real-time schema reasoning: event ordering, late-arriving data
03Onsite: data architecture
60 minDesign a pipeline for a marketplace or real-time system: surge pricing, fraud detection, driver earnings, ETA estimation.
- →Real-time vs batch tradeoff is central, know when each is appropriate
- →Uber's own stack leaks into prompts: Hudi, Kafka, Flink, Pinot
- →Operations matter: pager load, cost per query, incident frequency
04System design (pipeline architecture)
60 minDesign a production pipeline end-to-end: ingestion, transformation, storage, consumers, SLAs, failure modes, backfill strategy, and cost trade-offs. At senior level, you drive the conversation without prompting. Expect follow-ups about scale, cross-team coordination, and operational load.
- →Anchor on the SLA and data shape before diagramming
- →Discuss idempotency, partitioning, and backfill explicitly
- →Estimate cost: 'This pipeline will cost roughly $X/month at this volume'
05Onsite: behavioral + values
60 minUber values customers, team-first, and grit-under-pressure stories. The culture reset post-2017 emphasized respect and inclusivity.
- →Stories about pressure: tight deadlines, incidents, cross-team conflict
- →Customer obsession in Uber terms means drivers, riders, AND eaters
- →Avoid old-Uber hustle mythology, the culture has evolved
Level bar
What Uber expects at Senior Data Engineer
Independent technical leadership
Senior DEs drive pipeline designs without engineering manager involvement. Interviewers probe whether you can decompose ambiguous requirements, make architecture trade-offs, and defend your choices under scrutiny.
Cross-team coordination
Senior scope regularly spans multiple teams. Expect scenarios about a downstream team missing an SLA because of a change you made, or negotiating a schema migration with the team that owns the upstream source.
Production operational rigor
Fluent in on-call, alerting, data quality checks, and incident response. Dive-deep stories at this level should include correlating a metric drop to a specific commit or a timezone bug or a subtle ordering issue, not 'I looked at the logs.'
Uber-specific emphasis
Uber's loop is characterized by: Marketplace and real-time systems focus with operator-style pragmatism. Calibrate your preparation to that, generic FAANG prep will not close the gap on company-specific expectations.
Behavioral
How Uber frames behavioral rounds
Customer obsession
Uber's customers span riders, drivers, restaurants, and eaters. DEs are expected to think about all four.
Grit under pressure
Uber's operational tempo is intense. Stories about performing in high-stakes moments, launches, incidents, deadlines, resonate.
Building with the team
Post-2017 culture shift. Uber now emphasizes collaboration over lone-wolf heroics. Stories about enabling the team count.
Operator mindset
Uber values engineers who think like operators: cost, reliability, pager load, time-to-detect.
Prep timeline
Week-by-week preparation plan
Foundations and gap analysis
- ·Do 10 medium SQL problems. Note which patterns feel slow
- ·Write out 2-3 behavioral stories per value, Uber weights this round heavily
- ·Read Uber's public engineering blog for recent architecture patterns
- ·Review your prior production work, pick 3-5 projects you can discuss in depth
SQL and coding fluency
- ·Practice window functions until DENSE_RANK, ROW_NUMBER, LAG, LEAD are reflex
- ·Do 20+ Uber-style problems in their domain
- ·Time yourself: 25 min per medium, 35 min per hard
- ·Record yourself narrating approach aloud, communication is graded
Pipeline system design
- ·Design 5 pipelines on paper: daily aggregation, clickstream, CDC, ML feature store, real-time alerting
- ·For each, write SLA, partition strategy, backfill plan, and cost estimate
- ·Practice with a friend, senior-level system design is 50% driving the conversation
- ·Review Uber's open-source and engineering blog for in-house patterns
Behavioral polish and mock loops
- ·Rehearse every story out loud. Cut to 2-3 minutes each
- ·Run 2 full mock loops with a senior DE or coach
- ·Identify your 3 weakest behavioral areas and draft additional stories
- ·Review recent Uber news or earnings call for fresh talking points
Taper and logistics
- ·No new content. Review your notes only
- ·Sleep. Mental energy matters more than one more practice problem
- ·Confirm logistics: laptop charged, shared-doc tool tested, snack and water nearby
- ·Remember: the loop is rooting for you to raise the bar, not to fail
FAQ
Common questions
- What level is Senior Data Engineer at Uber?
- Senior Data Engineer maps to L5 on Uber's engineering ladder. This is an individual contributor level; expectations focus on independent technical leadership and cross-team influence.
- How much does a Uber Senior Data Engineer make?
- Based on 4 offer samples covering 2026, Uber Senior Data Engineer sees $276K at the 25th percentile, $368K at the median, and $441K at the 75th percentile, median base $212K and median annual equity $128K. Typical experience range: 8-10 years..
- How is the Senior Data Engineer loop different from other levels at Uber?
- The rounds look similar, but the bar calibrates to seniority. Senior Data Engineer is evaluated on independent technical leadership and cross-team influence. Questions at this level probe independent system design and cross-team influence.
- How long should I prepare for the Uber Senior Data Engineer interview?
- Plan for 8-10 weeks of prep if you're already a working DE. Under 4 weeks rushes the behavioral prep, which takes the most time.
- Does Uber interview data engineers differently than software engineers?
- They differ meaningfully. Uber's DE loop has heavier SQL, replaces the general system-design with a data-specific one (pipelines, warehouse design), and expects production data ops experience.