Airbnb Data Engineer Interview Guide (2025) – Process, Questions & Salary

Preparing for an Airbnb data engineer interview in 2025 means stepping into a role that is driving the company’s digital transformation and global expansion. You’ll be working on cutting-edge projects involving AI-powered personalization, real-time analytics, and massive-scale data infrastructure. As a candidate, you are expected to demonstrate not only technical expertise in tools like Airflow, Spark, and Kafka, but also the ability to design systems that support dynamic pricing, fraud detection, and personalized recommendations. Airbnb’s interview process has evolved to be more structured and collaborative, with a strong focus on business impact and communication. This guide will help you succeed in your Airbnb data engineer interview by giving you the insights, context, and strategies needed for 2025.

Role Overview & Culture

In 2025, landing a data engineer role at Airbnb means joining a company where your technical skills will directly influence millions of users worldwide. You’ll be part of a fast-moving, data-driven culture that prioritizes innovation, real-time insights, and collaborative problem-solving. As an Airbnb data engineer you will design and optimize data pipelines that power everything from AI-powered recommendations to business-critical metrics like Gross Booking Value. Your daily work will include managing workflows in Airflow, processing data with Spark, and ensuring quality with modern tools like Iceberg and Trino. Whether you’re mentoring junior engineers or collaborating with ML teams, your impact will be immediate and far-reaching. This guide will help you prepare for that opportunity with confidence and clarity.

Why This Role at Airbnb?

If you’re aiming for a data engineering role that truly rewards your ambition, Airbnb offers one of the most compelling opportunities in tech. In 2025, entry-level data engineers at Airbnb earn around $241K in total compensation, while principal roles can exceed $827K. You also get access to stock options, wellness programs, flexible work, and exclusive travel perks. As an engineer, you’ll work on well-supported systems that let you focus on impact, not burnout. You’ll even have access to ongoing learning through Data University Airbnb, where you can sharpen your technical and analytical skills alongside top talent. If you’re ready to build your career with a company that invests in you, this guide will show you how to stand out and succeed.

The Airbnb data engineer interview is a rigorous, multi-stage journey designed to assess your technical depth, problem-solving skills, and cultural alignment. The process consists of:

• Application Submission
• Recruiter Screen
• Technical Screen
• Virtual On-Site Data Loop
• Offer & Hiring Committee

Application Submission

Your journey begins with a resume and application review, where Airbnb’s recruiting team looks for strong SQL and Python skills, hands-on experience in data modeling, ETL, and distributed data systems, as well as a track record of measurable impact. Highlighting large-scale data projects, scalable architecture, and business outcomes on your resume will make you stand out. Tailor your application to reflect Airbnb’s mission and values—demonstrating not just technical prowess, but also creativity and community focus. This is the most competitive stage, so invest time in aligning your experiences with the role’s core requirements and Airbnb’s culture of belonging and innovation.

Recruiter Screen

If your application passes, you’ll have a 30-minute call with a recruiter who wants to understand your background, technical strengths, and motivation for joining Airbnb. Expect a deep dive into your experience and a data engineer introduce yourself prompt—this is your moment to deliver a sharp, 60-second pitch highlighting your technical stack, recent accomplishments, and passion for data-driven solutions. Be ready to discuss your most impactful projects and how they align with Airbnb’s challenges. The recruiter will also gauge your communication skills and cultural fit, so show genuine enthusiasm and a collaborative mindset.

Technical Screen (Coding & SQL)

The technical screen is a 30–60 minute virtual session focused on coding and SQL. You’ll solve HackerRank-style problems that test your ability to manipulate data structures, optimize algorithms, and write complex SQL queries involving joins and window functions. Expect to work through real-world Airbnb scenarios—such as analyzing booking data, calculating user engagement, or designing efficient ETL steps. Interviewers look for clean, well-commented code and clear explanations of your approach. This round is designed to filter in engineers who can handle the scale, complexity, and performance demands of Airbnb’s production data systems.

Virtual On-Site “Data Loop”

If you advance, you’ll join the virtual on-site loop—a series of four technical rounds with senior engineers and cross-functional partners. As a data engineer airbnb candidate, you’ll face sessions on Python/Spark coding, advanced data modeling, ETL architecture, and a behavioral interview. You’ll design scalable schemas for Airbnb’s booking or review systems, architect robust ETL pipelines, and solve hands-on coding challenges. The behavioral round explores your collaboration style, leadership, and alignment with Airbnb’s mission. Each round is data-backed and scenario-driven, letting you demonstrate both technical breadth and depth in building systems that power a global platform.

Offer & Hiring Committee

After your interviews, Airbnb compiles interviewer feedback within 24–48 hours. The hiring committee, including managers and senior engineers, reviews your performance, calibrates your level (E4–E6), and benchmarks your compensation against market data. For senior roles, additional discussions may assess your ability to mentor, own roadmaps, and define architectural standards. If successful, you’ll receive a competitive offer—typically within one to two weeks—reflecting Airbnb’s commitment to rewarding top data engineering talent with strong base pay, bonuses, and equity. You’ll also get transparent feedback and next steps, so you know exactly where you stand in the process.

To succeed in the Airbnb data engineer interview, you need to be prepared for a mix of technical and behavioral questions that assess your problem-solving skills, system design thinking, and alignment with Airbnb’s mission and data culture.

Coding / SQL Questions

Expect SQL-heavy and algorithmic challenges that test your ability to manipulate large-scale Airbnb datasets, optimize queries, and think like an airbnb data engineer solving business-critical problems:

1. Write a query to find the top five paired products and their names

To find paired products often purchased together, join the transactions and products tables to associate transactions with product names. Use a self-join on the combined table to identify pairs of products purchased by the same user at the same time. Filter out duplicate pairs and enforce alphabetical order for consistent pairing. Finally, group by product pairs, count occurrences, and sort to get the top five pairs.

2. Given the two tables, write a SQL query that creates a cumulative distribution of the number of comments per user.

To solve this, start by counting the frequency of comments per user by joining the users and comments tables. Then, group by frequency to create a histogram. Finally, use a self-join to calculate the cumulative distribution by summing up frequencies for all rows with a frequency less than or equal to the current row.

3. How many customers that signed up in January 2020 had a combined (successful) sending and receiving volume greater than $100 in their first 30 days?

To solve this, transform the payments table to combine sender and recipient data into one column. Then calculate the time difference between transactions and user sign-up dates, filter for users who signed up in January 2020 and transactions within their first 30 days, and sum successful transaction amounts. Finally, count users whose total transaction volume exceeds $100.

4. Find the number of possible triangles from a list of side lengths

To solve this, use the combinations function from the itertools package to generate all possible 3-side combinations from the list. Then, check each combination against the triangle inequality using a helper function. Count the combinations that satisfy the inequality to determine the number of possible triangles.

5. Find the number of possible triangles from a list of side lengths

To solve this, use the combinations function from the itertools package to generate all possible 3-side combinations from the list. Then, check each combination against the triangle inequality using a helper function. Count the combinations that satisfy the inequality to determine the number of possible triangles.

6. Implement a shortest path algorithm to find the shortest path in a graph

To solve this, Dijkstra’s algorithm can be used. Initialize distances for all nodes as infinity except the start node, and use a priority queue to explore nodes. Update distances for neighbors if a shorter path is found, and backtrack from the end node to construct the path.

Data Architecture & ETL Design Questions

In this section, you’ll face scenario-based questions that evaluate your ability to design scalable ETL pipelines, implement schema evolution, and work with tools like Iceberg, Kafka, and Spark.

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

To design an ETL pipeline for video data, start with primary metadata collection and indexing, which automates the aggregation of basic video information like author, location, and format. Next, use user-generated content tagging, either manually or scaled with machine learning, to enrich the dataset. Finally, employ binary-level collection for detailed analysis of video attributes like colors and audio, while balancing cost-effectiveness. Automated content analysis using machine learning techniques like image recognition and NLP can further enhance the pipeline.

8. How would you build an ETL pipeline to get Stripe payment data into the database so analysts can build revenue dashboards and run analytics?

To build an ETL pipeline for Stripe payment data, use APIs to extract payment data and load it into a staging area. Transform the data by cleaning, validating, and enriching it, ensuring it aligns with the schema of the target database. Finally, load the processed data into the internal data warehouse, enabling analysts to create dashboards and perform analytics. Automate the pipeline for scalability and reliability.

9. How would you architect an end-to-end CSV upload and ingestion system?

Design a CSV ingestion pipeline by enabling uploads via presigned URLs to object storage, followed by a distributed queue for processing. Use streaming parsing for large files and validate data for schema and anomalies. Store validated data in a columnar data warehouse or data lake for fast querying, and expose it through APIs for analytics. Ensure scalability, monitoring, and failure handling for robustness.

10. What kind of data analytics solution would you design for storing and querying raw clickstream data from Kafka with a two-year retention period?

For clickstream data, use Kafka for ingestion and transfer data to a scalable warehouse like Amazon Redshift for long-term storage. Optimize query performance with SORTKEY and DISTKEY configurations, and use Spark Streaming for efficient batch processing. Employ an orchestrator like Airflow for pipeline automation and monitoring, while handling edge cases like late data arrivals to ensure accuracy.

11. How would you design a pipeline that ingests images and PDFs of resumes and transforms them into queryable text data?

Design the pipeline by using OCR models to convert images and PDFs into text data. Store the text in a data mart for machine learning models and a data product for keyword tracking. Build a search API for recruiters to query candidates by keywords. Ensure the pipeline minimizes turnaround time and supports scalable querying for analytics and search functionalities.

Behavioral & Values Questions

These questions help Airbnb assess your collaboration style, product intuition, and cultural fit—including how you answer the data engineer introduce yourself prompt and reflect Airbnb’s core values in real-world situations:

12. How comfortable are you presenting your insights?

At Airbnb, data engineers often collaborate with cross-functional teams that rely on accessible and actionable insights. To answer this question, describe how you prepare data presentations and how you tailor them for different audiences. Mention any tools or formats you use to communicate effectively, whether in-person or virtually, and give examples that show your adaptability and confidence.

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

Airbnb values empathy and clarity in cross-team communication. Share an example where you faced difficulty translating technical details for a non-technical audience. Explain how you adjusted your communication style and took time to understand stakeholder priorities, ultimately improving collaboration.

14. Describe an analytics experiment that you designed. How were you able to measure success?

Airbnb uses data-driven experimentation to improve guest experience and operational efficiency. When answering this question, walk through a specific experiment you designed. Discuss how you defined success metrics, implemented testing methods such as A/B testing, and ensured statistical rigor to draw meaningful conclusions.

15. Why Do You Want to Work With Us

This question gauges your alignment with Airbnb’s culture and goals. Before the interview, research Airbnb’s recent initiatives, values like inclusivity and innovation, and the impact of their platform. In your response, express enthusiasm for contributing to those efforts and explain how the company’s mission and environment will help you grow and make a difference.

To excel in the Airbnb data engineer interview, you should approach your preparation with a blend of technical rigor, business context, and personal storytelling. Begin by mastering SQL and Python, as these are foundational to nearly every round. You’ll need to demonstrate fluency in data modeling, ETL pipeline design, and distributed data processing—skills that are central to Airbnb’s data engineering challenges.

Practice with real-world datasets and focus on building scalable, efficient solutions, as interviewers will probe your ability to handle the scale and complexity of Airbnb’s platform. Courses from the Airbnb Data University series can be invaluable, especially those covering advanced SQL, data visualization, and machine learning with Python or Hive, as these mirror the technical expectations and tools used internally.

Beyond technical prep, review your resume and be ready to discuss your most impactful projects in detail, focusing on how your work drove measurable business outcomes and supported cross-functional teams. Mock interviews and our platform are excellent for refining your coding and SQL problem-solving under time constraints. Don’t neglect behavioral preparation: Airbnb values collaboration, creativity, and alignment with its mission, so be prepared to share stories that highlight your adaptability and teamwork. Feel free to practice further with our AI Interviewer. With a data-backed, structured approach, you’ll be ready to showcase both your technical expertise and your ability to drive innovation as a data engineer at Airbnb.

What Is the Average Salary for an Airbnb Data Engineer?

Does Airbnb Have a Formal “Data University” for Engineers?

Yes, Airbnb has a formal in-house training initiative known as Data University Airbnb. It provides employees—including data engineers—with courses and resources to strengthen data literacy, analytics, and engineering best practices. This initiative reflects Airbnb’s commitment to continuous learning and upskilling. You can read more about it on the Interview Query’s Engineering & Data Science blog.

How Long Does the Airbnb Data Engineer Interview Process Take?

The Airbnb data engineer interview process typically spans three to five weeks. It begins with resume screening and recruiter outreach, followed by technical assessments in coding, SQL, data modeling, and system design. Behavioral interviews focus on values alignment and communication. Expect recruiter follow-ups at each stage to guide your progress and answer logistical questions.

Now that you understand the structure and expectations of the Airbnb data engineer interview, your next step is to build confidence through focused preparation. Start by exploring our Airbnb Data Engineer Learning Path to sharpen your technical and system design skills. Then, get inspired by Jeffrey Li’s success story who navigated the process with strategic prep and collaboration. Finally, practice with our hand-picked collection of Airbnb data engineer interview questions designed to match real-world scenarios. With the right mindset, tools, and preparation, you can turn this opportunity into a major milestone in your career.