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
Discuss the trade-offs between adopting a lakehouse architecture (e.g., Delta Lake, Iceberg) versus a traditional data warehouse (e.g., Redshift, BigQuery) for Airbnb's analytics needs. Address query performance for BI workloads, cost, streaming support, governance, and ease of self-service analytics.
HardTechnical
0 practiced
With limited engineering resources, propose a prioritization framework for migrating legacy Tableau and Looker dashboards to a centralized metrics layer. Include scoring criteria (usage, exec importance, complexity), migration waves, rollback strategies, and communication plans for affected teams.
HardTechnical
0 practiced
Design an incident response playbook for critical analytics pipeline outages that impact executive dashboards at Airbnb. Include detection and alerting, severity levels, roles (on-call, incident commander), escalation paths, communication templates for execs, temporary mitigation steps, and postmortem practices.
MediumTechnical
0 practiced
Design a monitoring dashboard to track the health and results of A/B tests at Airbnb. Include primary metrics, guardrail metrics, real-time alerts, and recommended visualization types. Explain how the BI team should support experimenters during analysis and post-experiment reporting.
EasyTechnical
0 practiced
Define 'observability' for analytics pipelines at Airbnb. Name and justify three specific signals or metrics you would monitor (e.g., pipeline lag, row-count delta, schema drift) to ensure dashboards remain accurate and fresh, and suggest reasonable alert thresholds or SLAs for each.
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