Uber Data Engineer Interview Guide (2025) – Questions, Process, and Tips

Understanding the Uber data engineer interview in 2025 begins with recognizing what sets Uber apart as a global technology leader. While the company spans over 70 countries and serves millions each month, what truly matters for aspiring engineers is how this scale drives technical complexity. Uber’s platform relies on real-time decisions, and behind those decisions is a data infrastructure that moves faster than almost any in the industry. This speed and scale define the kind of engineering talent Uber looks for.

The hiring process reflects this demand for depth. It includes coding assessments, technical interviews, system design challenges, and behavioral rounds. Altogether, it spans several weeks and evaluates both technical proficiency and the ability to build systems that operate flawlessly under massive load.

Role Overview & Culture

Being a data engineer at Uber means building systems that don’t just handle data but power real-world experiences. Every ride, delivery, and route is shaped by systems that you help build.

This role requires fluency in real-time platforms like Kafka and Flink, and deep experience with ETL systems built on Spark and Hadoop. But technical tools are only part of the equation. Uber’s culture expects engineers to think big while sweating the small details. Their eight core values guide decision-making, pushing for fast execution, safety-first thinking, and radical collaboration. You’ll partner with product teams and data scientists, and your decisions will be directly tied to business outcomes.

Why This Role at Uber?

Joining Uber as a data engineer means working on some of the most advanced systems in the industry. Beyond tech, you’ll design systems that process billions of events daily, yet are responsive enough to drive real-time decisions—whether that’s matching a rider with a driver or optimizing food delivery routes.

The compensation package reflects this complexity, with data engineers earning an average of $386,000 annually in the US, including substantial equity components and comprehensive benefits. Yet what sets this role apart is the career growth. Uber’s rapid expansion means that engineers can quickly move into leadership roles. And with exposure to open-source contributions, machine learning platforms, and global-scale architecture, your experience becomes a launchpad for long-term success in tech.

Now that you understand what makes this opportunity unique, let’s explore the interview process and the kinds of questions you should prepare for.

The Uber data engineer interview process in 2025 reflects the company’s scale and complexity. Artificial intelligence plays a central role in evaluating technical candidates, not just in resume screening but throughout the process. AI tools assess coding habits, communication clarity, and problem-solving depth in real time. This allows Uber to filter for engineers who can thrive in a fast-moving, data-intensive environment. Rather than rushing candidates through, the process unfolds over 4 to 6 weeks, like:

• Application Submission
• Recruiter Screening
• Technical / Online Assessment
• On-Site Loop
• Hiring Committee and Offer

Application Submission

This phase sets the tone. Uber places a premium on clarity, relevance, and evidence of impact. Your resume must show deep experience with Python, SQL, Java, or Scala—especially as they apply to ETL pipeline development and distributed systems. Tools like Apache Spark, Flink, Hive, and Presto should appear not as buzzwords but as part of real-world projects you’ve delivered.

Uber wants to see that you can turn data into decisions. This means showing how you’ve improved system performance, built scalable pipelines, or enhanced data quality. Real-time streaming systems like Kafka and cloud platforms such as AWS or GCP are crucial. If you have used these to drive measurable business outcomes, highlight that.

Recruiter Screening

Once your resume is shortlisted, a recruiter call (30 to 60 minutes) will focus on alignment. This isn’t about proving technical depth—it’s about how you think, communicate, and connect with Uber’s values.

Expect to discuss why you want to work at Uber, what excites you about their data scale, and how your past work aligns with their needs. It is your opportunity to demonstrate that you understand what it means to build fast, think globally, and act with data.

You will also review your tech stack briefly, confirm logistics, and discuss your willingness to work in Uber’s hybrid office setup. The implication here is clear: this is the checkpoint for mutual interest and cultural fit.

Technical / Online Assessment

This is your first real technical challenge in the Uber data engineer interview process. Conducted on CodeSignal, this 60 to 90-minute assessment includes 2 to 4 coding questions. Topics usually include Python or Spark programming, SQL transformations, and realistic ETL challenges.

More than just solving problems, the assessment reveals your thought process. Can you write clean, scalable code that accounts for edge cases? Can you optimize SQL queries that operate across massive data volumes? Candidates often face questions around rolling metrics, time-series anomaly detection, and dynamic resource handling—problems that mirror what Uber solves daily.

Your performance here determines whether you move forward. Passing this round means you’ve shown you can think like a data engineer operating at Uber’s scale.

On-Site Loop (Coding, System Design, Behavioral)

If you succeed in the online assessment, you’ll enter the on-site loop. This includes four focused rounds: two coding sessions, one system design discussion, and one behavioral interview.

Coding interviews dive deep into algorithms and data structures, particularly those related to Uber’s engineering problems—graph algorithms, stream processing, and memory-efficient transformations. System design interviews go further. You’ll be asked to architect real-time pipelines, design dashboards, or model data lakes for petabyte-scale systems.

Your Uber data engineer interview experience will be significantly enhanced by preparing for the behavioral round. Interviewers want to know how you’ve handled cross-team conflicts, driven large-scale initiatives, or responded to technical failures. The emphasis here is on how you act under pressure, not just how you think.

This stage is demanding. But it is also where you can demonstrate that you’re not just a builder—you’re someone who can own systems end-to-end.

Hiring Committee and Offer

The final decision is made by a panel of senior engineers and hiring managers. They evaluate your full performance across all rounds. For senior-level candidates, this may include an additional design round focused on cost optimization or system scalability under tight constraints.

At this point, Uber is looking for more than raw skill. They want signs of long-term potential. Can you grow into a tech lead? Can you influence architecture? Can you help Uber innovate faster while staying reliable?

If successful, you’ll receive an offer that reflects both the complexity of the role and your readiness to take on massive-scale challenges. Compensation is generous, but the real value lies in joining a company that sets the pace for real-time, data-driven engineering at a global level.

Interviewing for this role means preparing for technical challenges and behavioral conversations that test both depth and adaptability in Uber’s fast-moving data environment.

Coding / SQL Questions

Many Uber data engineer interview questions revolve around practical SQL and coding tasks that assess your ability to extract insights, optimize queries, and model real-world scenarios like “Top-K riders per city” or “Detect duplicate trips.”:

1. Get the top 3 highest employee salaries by department

To solve this, use the RANK() function with PARTITION BY department_id to rank salaries within each department. Filter ranks less than 4, join with the departments table to get department names, and concatenate first_name and last_name for full employee names. Finally, sort by department name (ascending) and salary (descending).

2. Compute the cumulative sales for each product

To compute cumulative sales, use a self-join on the sales table, matching rows based on product_id and ensuring the date in one instance is greater than or equal to the date in the other. Group the results by product_id and date, then calculate the cumulative sum using the SUM() function.

3. Given a table of students and their SAT test scores, write a query to return the two students with the closest test scores with the score difference.

To solve this, use a self-join on the scores table to compare each student’s score with every other student’s score. Filter out duplicate comparisons and calculate the absolute score difference. Order the results by score difference and alphabetically by student names, then limit the output to the top result.

4. Write a SQL query to create a histogram of the number of comments per user in the month of January 2020

To create the histogram, first, perform a LEFT JOIN between the users and comments tables to include users with zero comments. Filter the comments to include only those created in January 2020. Then, group by user ID to count the comments for each user. Finally, group by the comment count to calculate the frequency of each count.

5. Find the Missing Element

To find the missing element between two lists, use the XOR operation. XOR all elements of the full list and the missing list; identical elements cancel out, leaving the missing element as the result. This approach ensures (O(n)) time complexity and (O(1)) space complexity.

6. Extract unique values from a dictionary where values occur only once

To solve this, iterate through the dictionary values and count their occurrences. Then, filter out the values that appear only once and return them as a list.

Data-Architecture / System Design Questions

These questions evaluate how well you can architect scalable, fault-tolerant systems—a critical skill for any Uber data engineer working with real-time data and cloud-based infrastructure:

7. Given a task to design a data mart or data warehouse for a new online retailer, how would you approach the system design?

To design the system, start by identifying the business process, which primarily involves sales data for analytics and reporting. Define the granularity of events, identify dimensions (e.g., buyer, item, date, payment method), and establish facts like quantity sold, total paid, and net revenue. Finally, sketch a star schema to organize the data efficiently for querying.

8. Design a data pipeline for hourly user analytics

To build this pipeline, you can either query the data lake directly for each dashboard refresh (local queries) or aggregate and store the data in a reporting table for faster access (unified queries). Using SQL’s DATE_TRUNC function, you can group events by hour, day, and week to calculate active user counts. For scalability and efficiency, orchestrate the pipeline using tools like Apache Airflow and handle edge cases like delayed data with cut-off thresholds.

9. Redesign batch ingestion to real-time streaming for financial transactions

To transition from batch processing to real-time streaming, use a distributed messaging system like Apache Kafka for event ingestion, ensuring high throughput and durability. Employ stream processing frameworks such as Apache Flink for real-time analytics and fraud detection, integrating with scalable storage solutions like Amazon S3 for compliance and historical analysis. Ensure data integrity and exactly-once processing using Kafka’s transactional APIs and Flink’s checkpointing mechanisms, while deploying the system in highly available clusters for reliability and scalability.

10. Design the system supporting an application for a parking system

To design the parking application system, start by identifying functional requirements such as real-time price updates, user location tracking, nearby parking spot suggestions, and cost calculation. Non-functional requirements include scalability, reliability, and performance. Use a database to store parking spot details, integrate APIs for location tracking, and implement a caching mechanism for real-time price updates. Ensure the system supports machine learning outputs for dynamic pricing while maintaining user-friendly interfaces.

11. How would you collect and aggregate data for multimedia information, specifically when it’s unstructured data from videos?

To aggregate unstructured video data, start with primary metadata collection and indexing, which involves automating the extraction of basic metadata like author, location, and format. Next, use user-generated content tagging, which can be manual or scaled with machine learning for enriching datasets. Finally, binary-level collection analyzes intricate details like colors and audio, though it requires significant resources. Automated content analysis using machine learning techniques like image recognition and NLP can further enhance the dataset.

Behavioral or “Culture Fit” Questions

The Uber data engineer interview experience also includes behavioral rounds focused on collaboration, problem ownership, and how well your decision-making aligns with Uber’s core values and team dynamics:

12. Why Do You Want to Work With Us

As a data engineer, your motivation to join Uber should connect to the company’s scale, mission, and technical ambition. Emphasize how Uber’s focus on real-time data, global impact, and innovative platforms like Michelangelo align with your personal drive to build systems that solve meaningful, high-volume problems. Express enthusiasm for working in a culture that values speed, ownership, and data-first decisions.

13. How would you convey insights and the methods you use to a non-technical audience?

At Uber, data engineers often collaborate with PMs, operations teams, and business leaders who need clarity, not complexity. You must be able to distill streaming data metrics or ETL system performance into takeaways that guide business actions. Use analogies, clear visuals, and real-world implications to make technical trade-offs feel relevant and accessible.

14. What are your strengths and weaknesses?

This question at Uber is an opportunity to reflect on how your strengths directly support large-scale system design or data reliability under pressure. For example, you might highlight a strength in simplifying legacy data workflows or optimizing pipeline latency. When discussing a weakness, be honest yet solution-oriented, like learning to manage ambiguity better in rapid-release environments.

15. How comfortable are you presenting your insights?

Uber values engineers who can speak the language of both data and business. Whether explaining a performance anomaly in a real-time dashboard or proposing a new Flink-based streaming architecture, you should be comfortable turning data into narratives. Demonstrating your ability to present to cross-functional teams or execs shows you’re ready to influence decisions, not just systems.

16. Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

At Uber, misalignment can lead to inefficiencies across high-velocity product launches or global rollouts. If you’ve struggled to explain a data quality issue or a trade-off in system design, talk about how you recalibrated—maybe by creating more intuitive dashboards or reframing your explanation in business terms. Show that you learn from friction and adapt communication styles based on stakeholder needs.

Preparing for a data engineer role at Uber requires more than technical skill—it demands a mindset tuned to real-time scale, clear communication, and relentless problem-solving.

Study the Role & Culture

Start by understanding Uber’s engineering principles, such as fast iteration, system reliability, and global scale. Research their core values and how they shape team collaboration, especially in cross-functional projects. Learn how an Uber data engineer contributes to these goals by enabling real-time insights and data-driven decisions.

Practice Common Question Types

Begin with practicing Uber data engineer interview questions, focusing on SQL, data structures, system design, and distributed processing. Review real-world scenarios like stream joins, ETL bottlenecks, and optimizing query performance. Build fluency in solving problems that mirror Uber’s scale and complexity.

Think Out Loud & Ask Clarifying Questions

During interviews, speak through your reasoning. This shows how you approach problem-solving and respond under pressure. Asking thoughtful, clarifying questions also highlights your ability to engage with ambiguity—something valued in dynamic engineering teams.

Brute Force, Then Optimize

Start solving problems with any working solution, even if it’s not efficient. Once it’s functional, walk through how to refactor and scale it. This mirrors how real systems evolve and demonstrates your ability to move from MVP to production-quality code.

Mock Interviews & Feedback

Simulate interview conditions with peers or our AI Interviewer. Focus on timing, communication, and identifying blind spots in your approach. Honest feedback helps refine both your technical depth and your interview delivery.

What Is the Average Salary for a Data Engineer Role at Uber?

Where Can I Read More Discussion Posts on Uber’s Data Engineer Role Here in IQ?

You can explore real candidate experiences and tips by visiting our Uber discussion board or read specific insights in threads like This Uber Interview Breakdown and Uber Data Engineering System Design. These community posts offer first-hand advice and preparation strategies.

Are There Job Postings for Uber Data Engineer Roles on Interview Query?

Yes! You can find active listings for Uber data engineer openings on our Interview Query job board. Browse roles, filter by location or experience level, and apply directly to positions that match your background.

Landing a role at Uber as a data engineer means stepping into a high-impact position where real-time decisions and massive data pipelines define your day-to-day work. As you prepare, be sure to explore our full Data Engineer Learning Path to build the exact skills Uber expects. For motivation, check out Hanna Lee’s success story and how she navigated her interview journey. Finally, dive into our curated Data Engineer Interview Questions to sharpen your practice with problems that closely mirror what you’ll face in the real process. Good luck!