Staff Data Engineer, Tech Foundations
About The Position
Airbnb’s Enterprise Core Datasets team builds technical data foundations that help the company understand its operations, resource investments, and the value created by new technologies. Its work spans datasets for AI adoption, usage, and cost; Unit Economics; enterprise applications such as Jira; and critical company metrics including Gross Booking Value and Revenue. The Staff Data Engineer provides hands-on technical leadership for datasets that inform decisions across Airbnb. The role leads complex initiatives from problem definition and architecture through implementation, validation, and production operations, building extensible, reliable data foundations with clear definitions, traceability, and historical integrity. The position works with petabyte-scale datasets and contributes reusable frameworks, shared engineering tools, and standards across Airbnb’s data community. This position is US - Remote Eligible. Occasional work at an Airbnb office or attendance at offsites may be required, as agreed with the manager. The employee must live in a state where Airbnb, Inc. has a registered entity.
Requirements
- 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.
- Track record of leading complex data engineering initiatives from ambiguous requirements through architecture, delivery, and sustained production ownership.
- Deep expertise in SQL and strong programming skills in Python or Scala, with experience building maintainable, well-tested data systems.
- Extensive experience with distributed processing technologies such as Apache Spark and large-scale analytical storage systems.
- Strong data modeling skills, including fact and dimension design, slowly changing dimensions, incremental processing, and historical data management.
- Experience integrating heterogeneous sources, including APIs, enterprise applications, event data, and transactional systems, while managing schema changes and source reliability.
- Proven ability to build reliable pipelines and execute migrations and backfills while preserving correctness and downstream compatibility.
- Experience establishing data quality controls, observability, lineage, governance, and effective incident recovery practices.
- Strong performance engineering skills, including diagnosing bottlenecks and optimizing distributed computation, storage, and query execution.
- Ability to align stakeholders across engineering and business functions, communicate architectural tradeoffs, and drive decisions across team boundaries.
- Demonstrated technical leadership through mentorship, architectural guidance, and improvements to shared engineering practices.
- Curiosity and sound judgment when learning new domains and balancing immediate delivery needs with long-term system health.
Nice To Haves
- Experience with AI usage or cost measurement, cloud cost attribution, Unit Economics, enterprise application data, or financial and payments datasets.
- Familiarity with workflow orchestration, SQL transformation frameworks, and modern table formats such as Apache Iceberg.
Responsibilities
- Lead architecture, implementation, and operation of high-impact data pipelines for 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, and build consistent data models for those definitions.
- Evolve Unit Economics datasets to connect revenue and cost drivers so teams can understand product profitability and resource efficiency.
- Build reliable ingestion and transformation pipelines for non-platform systems such as Jira and other BizTech enterprise applications.
- Translate ambiguous requirements into source contracts, reusable fact and dimension models, and technical designs.
- Develop configurable pipelines and shared components to simplify onboarding of data sources, products, and business requirements.
- Establish automated testing, reconciliation, anomaly detection, and monitoring to protect data accuracy, completeness, and availability.
- Lead safe migrations and historical backfills through dependency analysis, validation, controlled cutovers, and recovery planning.
- Optimize distributed processing, storage, and query performance to improve data freshness and reduce compute costs.
- Own production reliability through on-call participation, incident response, and lasting improvements to systems and runbooks.
- Shape the technical roadmap, lead design reviews, mentor engineers, and contribute improvements to Airbnb’s Data Engineering Paved Path.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
