Uber Data Engineer
Interview Guide

📊 Quick Stats

Timeline: 4-6 weeks | Difficulty: Hard | Total Comp (L4): $170-230K | Reapply: 6-12 months

What makes it unique: Two-sided marketplace focus • Real-time operations emphasis • 8 cultural norms • Global-local balance

The Gist

Uber's analytics interview process is designed for candidates who thrive in fast-paced, ambiguous environments with real-time operational intensity. Unlike traditional tech companies that focus purely on product analytics, Uber emphasizes marketplace dynamics—you must think about rider AND driver behavior simultaneously, understanding how changes to one side ripple through the entire system.

The interview process spans 4-6 weeks and includes 5-6 rigorous rounds testing SQL proficiency, product sense, marketplace intuition, and cultural fit. The behavioral round carries significant weight, with deep dives into Uber's 8 cultural norms: We build globally, we live locally • We are customer obsessed • We celebrate differences • We do the right thing • We act like owners • We persevere • We value ideas over hierarchy • We make big bold bets.

Uber values scrappiness and speed over perfection. In interviews, you'll face real-world scenarios requiring 80% solutions delivered quickly rather than perfect analyses that come too late. Questions often involve operational urgency: "Bookings dropped 10% in Chicago this morning—what do you do?" This tests your ability to triage, hypothesize, and recommend action under pressure.

The technology stack centers on distributed systems (Presto, Kafka, Spark) handling billions of events daily. Strong SQL skills are non-negotiable, Python proficiency is expected, and familiarity with real-time analytics is highly valued. You'll learn Uber's proprietary tools on the job, but demonstrating experience with large-scale data systems gives you an edge.

Compensation is competitive with tier-1 tech companies: L3 (0-2 years): $130-170K | L4 (2-5 years): $170-230K | L5 (5-8 years): $230-330K. Uber's path to profitability and stock appreciation (100%+ from 2022 lows) has made equity packages increasingly valuable.

What Does an Uber Data Analyst Do?

As a data analyst at Uber, you're the analytical voice for one of the world's most complex two-sided marketplaces. Your work directly impacts 150+ million monthly riders, 6+ million drivers and couriers, and the real-time operations that keep this global network running 24/7 across 10,000+ cities.

Your day-to-day involves analyzing marketplace dynamics (supply-demand balance, pricing efficiency, matching quality), designing and evaluating A/B experiments to optimize the platform, building operational dashboards that city teams check hourly, and investigating metric movements in real-time ("Why did driver churn spike 15% in Miami today?").

The marketplace complexity sets Uber apart. Unlike single-sided businesses, every decision creates ripple effects. Lowering rider prices might increase demand, but if driver earnings drop, supply shrinks, causing surge pricing and worse rider experience. You must constantly optimize for multiple stakeholders while maintaining platform health.

Technology stack focuses on distributed systems: Presto for SQL, Kafka for real-time event streaming, Spark for large-scale processing, Python for analysis, and internal tools like XP (experimentation platform) and real-time operational dashboards. You'll work with billions of rows of data daily, requiring strong query optimization skills.

Career levels follow Uber's L-system: L3 for early-career (0-2 years, $130-170K), L4 for mid-level (2-5 years, $170-230K), L5 for senior (5-8 years, $230-330K), and L6+ for staff roles ($315K+). Progression is performance-based; high performers can advance faster than at traditional tech giants.

Before You Apply

What Uber Looks For

Uber evaluates candidates on technical capability, marketplace intuition, and cultural fit—all three are necessary, none alone is sufficient.

Technical requirements: Advanced SQL proficiency (complex joins, window functions, query optimization for billion-row tables), Python for data manipulation, statistical fundamentals (A/B testing, causal inference, confidence intervals), and comfort with large-scale distributed systems.

Marketplace intuition: Understanding two-sided network dynamics, network effects, liquidity, and multi-stakeholder optimization. Can you think about rider and driver behavior simultaneously? Do you grasp how pricing affects both sides? Can you identify unintended consequences of product changes?

Cultural alignment with Uber's 8 norms: Scrappiness (moving fast with imperfect data), ownership (proactive problem-finding), customer obsession (rider AND driver empathy), perseverance (pushing through ambiguity), and boldness (making high-impact bets with incomplete information).

Red flags that will sink your candidacy: Analysis paralysis (proposing 50 analyses instead of focusing on critical few), single-sided thinking (only optimizing for riders OR drivers, ignoring marketplace balance), perfectionism that slows velocity, hierarchical mindset (waiting for permission), and lack of operational urgency (treating everything as a quarterly strategic project when some decisions need answers in hours).

Prep Timeline

💡 Key Takeaway: Start SQL practice 3+ months early. Uber's questions test real marketplace scenarios with large-scale data, requiring both technical depth and business intuition.

3+ months out:

• Master SQL: window functions, complex joins, CTEs, query optimization
• Study marketplace dynamics: two-sided networks, liquidity, network effects
• Use Uber products daily (rides, eats) and think analytically about the experience
• Practice on LeetCode SQL, HackerRank, Skillvee

1-2 months out:

• Prepare 6-8 STAR stories aligned with Uber's 8 cultural norms
• Practice product sense questions (marketplace-specific scenarios)
• Study Uber's business model: unit economics, competitive landscape, regulatory challenges
• Mock interviews with peers or coaches

1-2 weeks out:

• Review your STAR stories and quantify all results
• Practice thinking out loud for SQL problems
• Research the specific team/product area you're interviewing for
• Prepare thoughtful questions for interviewers

Interview Process

⏱️ Timeline Overview: 4-6 weeks total

Format: 1 recruiter call → 1 technical screen → 5-6 hour onsite → decision

Uber's analytics interview has 5 stages:

1. Recruiter Screen (30 min)

Quick phone call to assess basic fit, discuss background, and gauge mutual interest.

Questions:

• "Why Uber?"
• "Tell me about your experience with marketplace or operational analytics"
• "What's your timeline and are you interviewing elsewhere?"

Pass criteria: Clear communication, relevant experience (especially marketplace/ops), genuine enthusiasm for Uber's mission.

2. Technical Phone Screen (60 min)

This round tests SQL proficiency and analytical thinking through live coding and a product/metrics discussion.

SQL Coding (35-40 min):

• 2-3 progressively harder problems
• Example: "Calculate weekly rider retention cohorts by city"
• Example: "Analyze surge pricing effectiveness by comparing pre/post-surge metrics"
• Expect: Complex JOINs, window functions, CTEs, time-series analysis

Product/Analytics (15-20 min):

• Python data manipulation (pandas) OR product sense question
• Example: "How would you measure success for Uber Pool?"
• Tests: Business intuition, metrics definition, marketplace thinking

🎯 Success Checklist:

• ✓ Think out loud—silence = red flag
• ✓ Ask clarifying questions before coding
• ✓ Write clean, commented SQL with proper formatting
• ✓ Handle edge cases (nulls, duplicates, time zones)
• ✓ Connect technical work to business context

3. Virtual Onsite (4-6 hours)

📋 What to Expect: 5-6 back-to-back 45-60 minute interviews

Breaks: 10 min between rounds

Format: Video call with shared coding environment

The onsite tests technical depth, analytical thinking, and cultural fit:

Round 1: Advanced SQL / Data Manipulation (60 min)

Focus: Technical depth with marketplace scenarios

• 2-3 progressively harder SQL problems
• Example: "Build a funnel showing rider drop-off from app open to trip completion, with conversion rates by city and cohort"
• Example: "Calculate driver utilization rates and identify potential churn based on declining trends"
• Expect: Window functions, recursive CTEs, optimization, handling billions of rows

Round 2: Product Analytics / Metrics Design (60 min)

Focus: Business judgment and framework

• Define metrics, design experiments, recommend actions
• Example: "Uber wants to increase retention in mid-sized markets. How would you approach this? What metrics matter? How would you test interventions?"
• Tests: Metric definition, experiment design, multi-stakeholder optimization, structured thinking

Round 3: Technical Deep Dive / Past Experience (60 min)

Focus: In-depth exploration of your most significant project

• Full hour discussing one analysis end-to-end
• Deep dive into methodology, challenges, impact
• Questions: "Why this approach?" "What alternatives did you consider?" "What was the business impact?"

Round 4: Behavioral / Cultural Fit (45-60 min)

Focus: Alignment with Uber's 8 cultural norms

Common questions organized by norms:

• Build globally, live locally: "Tell me about adapting your approach for different contexts"
• Customer obsessed: "How did you identify and solve a customer pain point with data?"
• Act like owners: "Give an example of taking ownership beyond your job description"
• Make big bold bets: "Walk me through a risky recommendation based on incomplete data"

💡 Pro Tip: Prepare 6-8 STAR stories covering all 8 cultural norms. Use the STAR method: Situation → Task → Action (60% of answer) → Result (quantified).

Round 5: Case Study / Analytics Problem (60 min)

Focus: End-to-end problem-solving

• Business problem requiring structured analytical approach
• Example: "Driver active hours declined 10% in Chicago. How do you investigate and what do you recommend?"
• Example: "Surge pricing complaints increased 30%. Walk me through your investigation."

Tests: Structured thinking, business judgment, analytical creativity, pragmatism

4. Hiring Committee / Decision (3-7 days)

Interviewers submit feedback and recommendations. Hiring manager makes final decision (some teams use committee process like Meta). Level is confirmed based on performance.

Outcomes:

• ✅ Strong hire → Offer
• 🔄 Hire with reservations → Possible additional interview
• ❌ No hire → 6-12 month cooldown

5. Offer and Negotiation (1-2 weeks)

Response timeline: 7-10 days (can request extension to 14 days)

Negotiation leverage:

• Competing offers (Lyft, DoorDash, Instacart carry most weight)
• Specialized marketplace/logistics experience
• Geographic flexibility

Most negotiable components:

• Stock/RSUs (high flexibility)
• Sign-on bonus (very high flexibility)
• Base salary (low-medium flexibility)

Key Topics to Study

SQL (Critical)

⚠️ Most Important: Window functions + query optimization for large datasets. Nearly every Uber SQL interview tests ability to write efficient queries on billion-row tables.

Must-know concepts:

• JOINs (inner, left, self-joins, complex multi-table joins)
• Window functions (ROW_NUMBER, RANK, LAG/LEAD, rolling aggregates)
• CTEs and subqueries
• Aggregations with GROUP BY, HAVING
• Date/time manipulation (time zones, date arithmetic)
• Query optimization (avoiding cross-joins, using appropriate indexes, partitioning)

Practice platforms: LeetCode SQL, HackerRank, Skillvee, DataLemur

Marketplace Dynamics (Critical)

Core concepts:

• Two-sided networks: supply (drivers) and demand (riders)
• Liquidity: ensuring adequate supply to meet demand
• Network effects: more riders attract more drivers, more drivers reduce wait times, attracting more riders
• Multi-stakeholder optimization: balancing rider experience, driver earnings, and Uber's take rate
• Pricing mechanisms: surge pricing, dynamic pricing, upfront pricing

Application in interviews:

• "How does lowering rider prices affect driver supply?"
• "What metrics indicate healthy marketplace liquidity?"
• "Design an experiment to test a new pricing model without harming either side"

Product & Metrics (Critical)

Frameworks:

• Metric definition (north star metrics, guardrail metrics, diagnostic metrics)
• A/B test design (hypothesis, randomization, sample size, significance)
• Root cause analysis (data quality → trends → segmentation → external factors)
• Funnel analysis (conversion rates, drop-off points, optimization)

Common Uber metrics:

• Rider metrics: Bookings, completed trips, retention, NPS, wait time, cancellation rate
• Driver metrics: Active hours, utilization rate, earnings per hour, acceptance rate, churn
• Marketplace metrics: Supply-demand ratio, surge frequency, matching efficiency

Statistics & A/B Testing (Important)

Core concepts:

• Hypothesis testing, p-values, confidence intervals
• Type I/II errors, statistical power
• Sample size calculation
• Multiple testing corrections (Bonferroni, FDR)
• Causal inference basics

Red flags to avoid:

• Peeking at results early (p-hacking)
• Ignoring seasonality or day-of-week effects
• Confusing statistical significance with practical significance
• Not accounting for network effects in marketplace experiments

Behavioral Questions (Critical)

Prepare 6-8 STAR stories covering Uber's 8 cultural norms:

We build globally, we live locally:

• "Tell me about adapting your approach for different contexts"

We are customer obsessed:

• "How did you identify a customer pain point through data?"

We celebrate differences:

• "Describe when diverse perspectives improved your analysis"

We do the right thing:

• "Tell me about finding a data quality or ethical issue. What did you do?"

We act like owners:

• "Give an example of taking ownership beyond your scope"

We persevere:

• "Describe pushing through major project roadblocks"

We value ideas over hierarchy:

• "Tell me about challenging a senior decision with data"

We make big bold bets:

• "Walk me through a risky recommendation with incomplete data"

Structure: Situation → Task → Action (focus 60% here) → Result (quantified + learning)

Compensation (2025)

💰 Total Compensation Breakdown

All figures represent total annual compensation (base + stock/4 + bonus)

Location Adjustments:

• 🌉 Bay Area: 1.00x (baseline)
• 🌲 Seattle: 0.95x
• 🗽 NYC: 0.98x
• 🤠 Austin: 0.85x
• 🏠 Remote: 0.75-0.90x

🎯 Negotiation Strategy:

• Stock and sign-on bonuses are most flexible (base is more rigid)
• Competing offers from Lyft, DoorDash, Instacart provide strongest leverage
• Marketplace/logistics analytics experience increases negotiating power
• Realistic increase with strong negotiation: $30-70K

Equity Vesting:

• 4-year vesting with 25% cliff at 1 year
• Quarterly vesting after cliff (6.25% every 3 months)
• Annual refresh grants starting year 2 (10-30% of initial, up to 60% for top performers)

Benefits Package:

• Flexible PTO (typical usage: 15-20 days/year L3-L4, 20-25 L5+)
• 18 weeks paid parental leave (primary caregiver), 6 weeks (secondary)
• 401(k) with 4% match (immediate vesting)
• $200-300/month Uber and Uber Eats credits
• Comprehensive health, dental, vision
• $1-2K/year education stipend

Your Action Plan

Ready to start preparing? Here's your roadmap:

📚 Today:

1. Assess your SQL level with marketplace-themed practice problems
2. Use Uber and Uber Eats—think analytically about the experience
3. Start drafting STAR stories from past experiences

📅 This Week:

1. Set up a 3-6 month study schedule
2. Create a SQL practice plan (window functions, optimization, large-scale data)
3. Draft 6-8 STAR stories aligned with Uber's 8 cultural norms

🎯 This Month:

1. Complete 30-40 SQL problems (focus on marketplace scenarios)
2. Study two-sided marketplace dynamics and unit economics
3. Practice 10-15 product sense questions out loud
4. Schedule mock interviews

🚀 Ready to Practice?

Browse Uber-specific interview questions and take practice interviews to build confidence and get real-time feedback.