Staff Data Engineer (Tech Foundations)

Join Airbnb as a Staff Data Engineer, where you will lead the architecture, implementation, and operation of high-impact data pipelines. You will partner with stakeholders to define AI adoption metrics, evolve Unit Economics datasets, and build reliable ingestion and transformation pipelines. You will also optimize distributed processing, storage, and query performance, and own production reliability. This role requires strong data modeling skills, extensive experience with distributed processing technologies, and a track record of leading complex data engineering initiatives.

- Lead the architecture, implementation, and operation of high-impact data pipelines across AI usage and cost, Unit Economics, enterprise applications, and critical Financial metrics. - Partner with stakeholders to define how AI adoption, consumption, and spending are measured across the organization, and build consistent data models that support those definitions. - Evolve Unit Economics datasets to connect revenue and diverse cost drivers, enabling teams to understand product profitability and resource efficiency.

- Strong data modeling skills, including fact and dimension design, slowly changing dimensions, incremental processing, and historical data management - Extensive experience with distributed processing technologies such as Apache Spark and large-scale analytical storage systems - 9+ years of relevant industry experience with a Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience; or 6+ years of relevant experience with a PhD - Strong performance engineering skills, including diagnosing bottlenecks and optimizing distributed computation, storage, and query execution - Deep expertise in SQL and strong programming skills in Python or Scala, with experience building maintainable, well-tested data systems - Demonstrated technical leadership through mentorship, architectural guidance, and improvements to shared engineering practices - A track record of leading complex data engineering initiatives from ambiguous requirements through architecture, delivery, and sustained production ownership - Curiosity and sound judgment when learning new domains and balancing immediate delivery needs with long-term system health - Experience establishing data quality controls, observability, lineage, governance, and effective incident recovery practices - Experience integrating heterogeneous sources, including APIs, enterprise applications, event data, and transactional systems, while managing schema changes and source reliability - The ability to align stakeholders across engineering and business functions, communicate architectural tradeoffs, and drive decisions across team boundaries - Proven ability to build reliable pipelines and execute migrations and backfills while preserving correctness and downstream compatibility - Experience with AI usage or cost measurement, cloud cost attribution, Unit Economics, enterprise application data, or financial and payments datasets is a plus. Familiarity with workflow orchestration, SQL transformation frameworks, and modern table formats such as Apache Iceberg is also valuable