Uber Staff Data Engineer Interview (L6)
At Uber, the (L6) Staff Data Engineer interview is characterized by Marketplace and real-time systems focus with operator-style pragmatism. To clear this bar you need organizational impact beyond a single team and tech strategy ownership, built on 8-12 years of production DE work.
Compensation
$235K–$295K base • $500K–$720K total (L6)
Loop duration
4 hours onsite
Rounds
5 rounds
Location
San Francisco, NYC, Sunnyvale, Seattle, Chicago
Compensation
Uber Staff Data Engineer total comp
Offer-report aggregate, 2021-2026. Level mapped: L6. Typical experience: 6-10 years (median 8).
25th percentile
$379K
Median total comp
$523K
75th percentile
$575K
Median base salary
$247K
Median annual equity
$244K
Practice problems
Uber staff data engineer practice set
Uber staff data engineer practice set, mapped from predicted domain emphasis. Tap into any problem to work it in the live environment.
The Switchboard
A feature-flag service writes a new record every time a flag is switched on or off, so one flag can appear many times with different settings. For each flag, give its current state as two indicators: a 1/0 for whether it is enabled and a 1/0 for whether it is disabled.
What Won't Convert
A raw export hands you a column that should be integers, but some cells hold junk that won't convert. Return the values as integers in their original order, with `None` in place of anything that can't be converted.
The Shape of a Run
Every machine in our fleet emits one log line when a process starts and a separate line when it stops, and each line records the machine, a process id the machine assigns locally, which kind of event it was, and a timestamp in float seconds. The warehouse has to keep every line exactly as it arrived so analysts can reconcile a start with its matching stop themselves, computing the average elapsed time per process, drawing per-machine timelines of every process in order, and flagging starts that never got a stop. Design the data model behind this log and describe how the daily files load in through an ETL.
What Everyone Is Watching
We need to track what our subscribers are watching. This data feeds everything from our recommendation models to operations dashboards that monitor playback quality in real time. Design a data pipeline for our viewing 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
04Architecture strategy
60 minAt staff level, system design expands to multi-system strategy: 'Design the data platform for a 500-person org' or 'We have 40 pipelines producing inconsistent output; how do you fix it?' The evaluator watches for whether you think about developer experience, tech-debt paydown, and multi-quarter roadmaps.
- →Talk about teams and processes, not just technology
- →Name the specific mechanisms you would create (code review standards, shared libraries, data contracts)
- →Be ready to defend why not to build something you would build at senior level
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 L6 Staff Data Engineer
Technical strategy ownership
Staff DEs set technical direction for multiple teams. Interviewers ask 'What tech decisions have you influenced across your org?' and probe depth: how did you socialize it, who pushed back, what trade-offs did you accept?
Multi-system design
Staff-level design is not one pipeline; it is the platform that 10 pipelines run on. Think data contracts, metadata stores, standardized ingestion patterns, shared orchestration, and the tradeoffs between standardization and team autonomy.
Tech-debt and migration leadership
Stories about leading a multi-quarter migration: the plan, the phasing, the stakeholder management, the rollback criteria. Staff DEs are expected to have shipped at least one such effort.
Mentorship scale
At staff, mentorship goes beyond 1:1 coaching: you have influenced hiring rubrics, run tech talks, or built onboarding that accelerated new hires.
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 4.
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 your approach aloud. Interviewers weigh how you explain it, not only what you write
Platform-level system design
- ·Design 3-5 multi-system platforms: metadata store, shared ingestion, governance layer
- ·Prepare 2-3 stories where you drove technical direction across teams
- ·Practice mock interviews with another staff+ engineer
- ·Review Uber's publicly described platform work for recent architectural shifts
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
See also
Related interview guides
FAQ
Common questions
- What level is Staff Data Engineer at Uber?
- Staff Data Engineer maps to L6 on Uber's engineering ladder. This is an individual contributor level; expectations focus on organizational impact beyond a single team and tech strategy ownership.
- How much does a Uber Staff Data Engineer make?
- Based on 6 offer samples covering 2021-2026, Uber Staff Data Engineer sees $379K at the 25th percentile, $523K at the median, and $575K at the 75th percentile, median base $247K and median annual equity $244K. Typical experience range: 6-10 years..
- How is the Staff Data Engineer loop different from other levels at Uber?
- The rounds look similar, but the bar calibrates to seniority. Staff Data Engineer is evaluated on organizational impact beyond a single team and tech strategy ownership. Questions at this level probe multi-team technical strategy and platform thinking.
- How long should I prepare for the Uber Staff Data Engineer interview?
- Plan for 10-12 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.