Airbnb Fit and Data Engineering Vision Questions
Topic focusing on understanding Airbnb's cultural fit and how the company's data engineering vision shapes its product strategy, data platform, governance, and cross-functional collaboration. Discuss how a candidate's values, communication style, and approach align with Airbnb's culture while considering the data engineering direction.
MediumTechnical
0 practiced
Given a streaming 'user_events' topic and a 'bookings' table, write a Postgres-compatible SQL or pseudo-code to compute rolling average time from first-session-to-first-booking per user for the last 90 days. Account for late-arriving events and users with no bookings (should appear with null or 0 as appropriate).
HardTechnical
0 practiced
A production A/B test shows a statistically significant drop in bookings correlated with a new ML ranking model rolled out to 20% of traffic. Outline an incident response: immediate actions (data collection, rollback thresholds), deeper root-cause analysis steps, how to coordinate across ops/product/legal, and communication plans for external stakeholders such as hosts.
HardTechnical
0 practiced
You're leading an initiative to standardize feature engineering across Airbnb teams. Create a 6–12 month roadmap covering stakeholder engagement, technical standards (APIs, schema), migration strategy for existing features, pilot projects, and success metrics to know when the program should be expanded globally.
MediumTechnical
0 practiced
Label noise can severely harm models for predicting cancellations. Describe strategies to detect label noise at scale and remediate it in data pipelines and model training for Airbnb. Mention approaches such as automated label auditing, noise-robust loss functions, and active labeling.
EasyBehavioral
0 practiced
How do you prefer to receive technical feedback in a cross-functional team environment? Provide a short real example where feedback changed how you built an ML pipeline or model and what you learned from it.
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