Staff Data Engineer, Tech Foundations

Airbnb•San Francisco, CA

2d•Remote

About The Position

The Enterprise Core Datasets team builds technical data foundations that help Airbnb understand its operations, resource investments, and the value created by new technologies. Its work spans datasets for measuring AI adoption, usage, and cost; Unit Economics; data from Jira and other enterprise applications; and critical company metrics such as Gross Booking Value and Revenue. The Staff Data Engineer provides hands-on technical leadership for datasets that shape decisions across Airbnb. This role leads complex data initiatives from problem definition and architecture through implementation, validation, and production operations. The engineer builds extensible, dependable data foundations and reusable frameworks, improves data correctness, freshness, performance, and cost efficiency, and contributes to shared engineering tools and standards. The position works with engineering, product, analytics, finance, and business teams.

Requirements

At least 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; alternatively, at least 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, including 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 across AI usage and cost, Unit Economics, enterprise applications, and financial metrics.
Partner with stakeholders to define how AI adoption, consumption, and spending are measured and build consistent supporting data models.
Evolve Unit Economics datasets to connect revenue and cost drivers for analysis of product profitability and resource efficiency.
Build reliable ingestion and transformation pipelines for non-platform systems such as Jira and other 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 freshness and reduce compute costs.
Own production reliability through on-call participation, incident response, and improvements to systems and runbooks.
Shape the technical roadmap, lead design reviews, mentor engineers, and contribute improvements to Airbnb’s Data Engineering Paved Path.