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 in Chicago make?

Based on 5 offer samples covering 2020-2026, Uber Senior Data Engineer in Chicago sees $215K at the 25th percentile, $279K at the median, and $373K at the 75th percentile, median base $174K and median annual equity $67K. Typical experience range: 6-7 years..

Does Uber actually hire data engineers in Chicago?

Yes, Uber maintains a Chicago office and hires Senior Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.

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.

Interview Guide · 2026

Uber Senior Data Engineer Interview in Chicago (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. This guide covers the Chicago (Chicago, IL) hiring office, including local compensation bands and market context.

Compensation

$160K–$201K base • $295K–$410K total (L5)

Loop duration

4 hours onsite

Rounds

5 rounds

Location

Chicago, IL

Compensation

Uber Senior Data Engineer in Chicago total comp

Across 5 samples

Offer-report aggregate, 2020-2026. Level mapped: L5. Typical experience: 6-7 years (median 7).

25th percentile

$215K

Median total comp

$279K

75th percentile

$373K

Median base salary

$174K

Median annual equity

$67K

Practice problems

Uber senior data engineer practice set

4 problems

Uber senior data engineer practice set, mapped from predicted domain emphasis. Tap into any problem to work it in the live environment.

SQLmedium~12 min

Second Highest Cloud Cost

Return the second-highest distinct amount value in cloud_costs. Return a single number.

Open in practice environment→

Pythoneasy~10 min

The Letter Ledger

Given a string, return a list of [character, count] pairs for each distinct alphabetic character. Treat case-insensitively (lowercase everything before counting). Sort the pairs alphabetically by character.

Open in practice environment→

Modelingmedium~30 min

The Retail Tables That Need a New Home

You are given an existing transactional database from a retail operation covering orders, customers, products, stores, and employees. The analytics team cannot write performant queries against this structure. Redesign it as a dimensional warehouse that supports reporting on sales performance, product mix, and customer behavior.

Open in practice environment→

Architecturemedium~20 min

Real-Time POS Ingestion into Snowflake

Our retail stores run point-of-sale terminals that generate transactions all day. The business intelligence team currently gets a nightly batch of sales data but they want same-day visibility. We also have years of historical sales in our Snowflake warehouse that needs to stay consistent with whatever we build. Design a pipeline to bring POS data into Snowflake in near-real-time.

Open in practice environment→

Try itRolling 7-day active users

Count distinct users active in the trailing 7 days for each date. Product analytics staple.

rolling_7dau.sql

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WITH dates AS (
  SELECT DISTINCT activity_date FROM activity
)
SELECT
  d.activity_date AS day,
  COUNT(DISTINCT a.user_id) AS rolling_7d_users
FROM dates d
JOIN activity a
  ON a.activity_date <= d.activity_date
  AND julianday(d.activity_date) - julianday(a.activity_date) < 7
GROUP BY d.activity_date
ORDER BY d.activity_date;
WITH dates AS (
  SELECT DISTINCT activity_date FROM activity
)
SELECT
  d.activity_date AS day,
  COUNT(DISTINCT a.user_id) AS rolling_7d_users
FROM dates d
JOIN activity a
  ON a.activity_date <= d.activity_date
  AND julianday(d.activity_date) - julianday(a.activity_date) < 7
GROUP BY d.activity_date
ORDER BY d.activity_date;

Click Run to execute. Edit the code above to experiment.

Chicago, IL

Uber in Chicago

Trading firms (Citadel, Jump, Jane Street) compete aggressively for DEs. Enterprise tech (McDonald's, United, Walgreens) also hires locally.

Offers in Chicago typically trail the reference band by around 18%, reflecting a lower cost of living. Chicago candidates run the same loop as global peers; the differences show up in team assignment and local comp calibration.

The loop

How the interview actually runs

01Recruiter screen

30 min

Standard 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 min

SQL 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 min

Design 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 min

Design 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 min

Uber 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.

“How has your data work affected a real customer's experience?”

Grit under pressure

Uber's operational tempo is intense. Stories about performing in high-stakes moments, launches, incidents, deadlines, resonate.

“Tell me about a time everything was breaking and you had to deliver anyway.”

Building with the team

Post-2017 culture shift. Uber now emphasizes collaboration over lone-wolf heroics. Stories about enabling the team count.

“Describe a time you made your teammates better.”

Operator mindset

Uber values engineers who think like operators: cost, reliability, pager load, time-to-detect.

“What operational concerns have you fixed in a data system?”

Prep timeline

Week-by-week preparation plan

8-10 weeks out

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

6 weeks out

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

4 weeks out

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

2 weeks out

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

Week of

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

Related interview guides

Uber Senior Data Engineer (global guide)→Uber Junior Data Engineer in Chicago→Uber Data Engineer in Chicago→Uber Staff Data Engineer in Chicago→Uber Principal Data Engineer in Chicago→Uber Senior Data Engineer in San Francisco Bay Area→Uber Senior Data Engineer in New York→Uber Senior Data Engineer in Seattle→Uber Data Engineer Salary→Data Engineer Interview Prep (hub)→

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 in Chicago make?: Based on 5 offer samples covering 2020-2026, Uber Senior Data Engineer in Chicago sees $215K at the 25th percentile, $279K at the median, and $373K at the 75th percentile, median base $174K and median annual equity $67K. Typical experience range: 6-7 years..
Does Uber actually hire data engineers in Chicago?: Yes, Uber maintains a Chicago office and hires Senior Data Engineer data engineers there. Team assignment may be office-locked or global; confirm with the recruiter before the loop.
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.

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Published 2026-04-20Updated 2026-04-20