Airbnb Data Engineer interview typically runs 6 rounds: take-home HackerRank, DSA coding, general coding, data modeling, behavioral, and system design. It usually takes a few weeks and is broad, covering both hands-on implementation and architecture.
$63K
Avg. Base Comp
$457K
Avg. Total Comp
6
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Airbnb is looking for more than someone who can write queries or ship pipelines in isolation. The signal that keeps showing up is breadth with judgment: the work starts with a SQL-heavy take-home, but the later conversations quickly widen into coding, modeling, and end-to-end design. That combination tells us the team wants data engineers who can move comfortably from implementation details to the structure of the system, and who can explain why a design choice fits a marketplace business rather than just making it work technically.
A recurring theme is that the hardest moments come when the interviewer pushes beyond syntax and into tradeoffs. Multiple candidates described the modeling and system design discussions as less about grinding through code and more about how you think about data flow, aggregation, and reliability. The questions themselves — from trial user segmentation to payment and booking pipelines — point to a strong interest in real product data, especially around how Airbnb would represent and reconcile marketplace events. We’ve seen that candidates who do best are the ones who can connect their solution back to the business shape of the data, not just the schema.
What makes this process non-obvious is that it is broad without feeling random. The mix of live coding, modeling, and behavioral evaluation suggests Airbnb is screening for engineers who can operate across layers and stay composed when the problem shifts. In our view, the hidden bar is not simply technical correctness; it’s whether you can make clean decisions under pressure and defend them in a way that feels practical for a large, distributed data platform.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Airbnb process.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
| Question | |
|---|---|
| Trial User Segmentation | |
| Listing Bookings Aggregation | |
| Data Pipelines and Aggregation | |
| String Palindromes | |
| Payment Data Pipeline | |
| Experiment Validity | |
| 2nd Highest Salary | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Download Facts | |
| Random SQL Sample | |
| Over-Budget Projects | |
| Month Over Month | |
| User Experience Percentage | |
| The Brackets Problem | |
| Third Purchase | |
| Permutation Palindrome | |
| Completed Shipments | |
| Rectangle Overlap | |
| Christmas Dinner Ingredient Optimization | |
| Google Maps Improvement | |
| Distance Traveled | |
| Target Indices | |
| Marketing Channel Metrics | |
| Hurdles In Data Projects | |
| Skyscanner Partner ETL | |
| Groups of Anagrams | |
| Repeat Job Postings | |
| Random Forest Explanation |
Synthesized from candidate reports. Individual experiences may vary.
The process started with a take-home HackerRank focused on SQL and Python. Candidates completed 3 to 4 SQL questions and 2 Python coding questions, with an emphasis on solving the problems cleanly and efficiently.
After the take-home, candidates moved to a virtual onsite with five separate interviews. The rounds covered DSA-style coding, general coding, data modeling, behavioral questions, and an end-to-end system design discussion, testing both hands-on implementation and broader architectural thinking.
Close preparation with examples that show ownership, communication, and how you work with cross-functional partners or technical peers. The available candidate evidence is sparse, so this stage is framed as a practical preparation bucket rather than a claim that every candidate saw a separate formal round. Where the source evidence blended final steps together, this stage captures the final evaluation themes without adding unsupported company-specific claims.