What level is Data Engineer at Uber?

Data Engineer maps to L4 on Uber's engineering ladder. This is an individual contributor level; expectations focus on shipped production pipelines end-to-end and can debug them when they break.

How much does a Uber Data Engineer make?

Based on 61 offer samples covering 2021-2026, Uber Data Engineer sees $133K at the 25th percentile, $219K at the median, and $309K at the 75th percentile, median base $158K and median annual equity $60K. Typical experience range: 4-9 years..

How long should I prepare for the Uber Data Engineer interview?

Plan for 6-8 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 Data Engineer Interview (L4)

At Uber, the (L4) Data Engineer interview is characterized by Marketplace and real-time systems focus with operator-style pragmatism. To clear this bar you need shipped production pipelines end-to-end and can debug them when they break, built on 2-5 years of production DE work.

Compensation

$160K–$195K base • $240K–$330K total (L4)

Loop duration

3 hours onsite

Rounds

4 rounds

Location

San Francisco, NYC, Sunnyvale, Seattle, Chicago

Compensation

Uber Data Engineer total comp

Across 61 samples

Offer-report aggregate, 2021-2026. Level mapped: L4. Typical experience: 4-9 years (median 6).

25th percentile

$133K

Median total comp

$219K

75th percentile

$309K

Median base salary

$158K

Median annual equity

$60K

Median total comp by year

2022

$134K n=3

2025

$220K n=20

2026

$215K n=35

Practice problems

Uber data engineer practice set

4 problems

Uber 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→

Pythonmedium~20 min

The Coin Vault

Given a target amount and a list of coin denominations, return the minimum coins needed using a greedy strategy: repeatedly take the largest coin that does not exceed the remaining amount. Return -1 if the greedy approach cannot make exact change.

Open in practice environment→

Modelingeasy~20 min

Event Ticketing System Data Model

We run an IT helpdesk platform. Users submit support tickets, which are assigned to agents. Tickets go through multiple status changes before being resolved. SLA compliance is critical: P1 tickets must be resolved within 4 hours, P2 within 24 hours. Design the schema, and describe how you would load data from a JSON API feed into it.

Open in practice environment→

Architecturemedium~25 min

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.

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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32












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.

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

04Onsite: 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 Data Engineer

Pipeline ownership

Mid-level DEs own pipelines end-to-end. Interviewers expect stories about designing, deploying, and maintaining a data pipeline that has been in production for 6+ months.

SQL + Python or Spark fluency

SQL is the floor. Most teams also expect fluency in either Python for data manipulation (pandas, airflow DAGs) or Spark for larger-scale processing.

On-call debugging

You should have concrete stories about production incidents: what alert fired, how you diagnosed, what you fixed, and what post-mortem action you owned.

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 awareness and behavioral depth

·Review pipeline architecture basics: idempotency, partitioning, backfill
·Practice explaining a pipeline you've worked on end-to-end in 5 minutes
·Refine behavioral stories based on mock feedback
·Do 10 more SQL problems at medium difficulty

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 mid-level 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: interviewers want to find reasons to hire you, not to reject you

Related interview guides

Uber Junior Data Engineer→Uber Senior Data Engineer→Uber Staff Data Engineer→Uber Principal Data Engineer→Amazon Data Engineer→Meta Data Engineer→Google Data Engineer→Uber Data Engineer Salary→Data Engineer Interview Prep (hub)→

FAQ

Common questions

What level is Data Engineer at Uber?: Data Engineer maps to L4 on Uber's engineering ladder. This is an individual contributor level; expectations focus on shipped production pipelines end-to-end and can debug them when they break.
How much does a Uber Data Engineer make?: Based on 61 offer samples covering 2021-2026, Uber Data Engineer sees $133K at the 25th percentile, $219K at the median, and $309K at the 75th percentile, median base $158K and median annual equity $60K. Typical experience range: 4-9 years..
How is the Data Engineer loop different from other levels at Uber?: The rounds look similar, but the bar calibrates to seniority. Data Engineer is evaluated on shipped production pipelines end-to-end and can debug them when they break. Questions at this level probe production pipeline ownership and on-call debugging.
How long should I prepare for the Uber Data Engineer interview?: Plan for 6-8 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.

Continue your prep

Data Engineer Interview Prep, explore the full guide

50+ guides covering every round, company, role, and technology in the data engineer interview loop. Grounded in 2,817 verified interview reports across 929 companies, collected from real candidates.

Interview Rounds

By Company

By Role

By Technology

Decisions

Question Formats

Start your data engineer interview prep:The Complete Interview Prep Guide Practice Problems Mock Interviews SQL and Python Lessons Daily Challenges

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