Staff Data Engineer

Who you are

• 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
• A 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
• The 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
• 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

What the job involves

• 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
• Build reliable ingestion and transformation pipelines for non-platform systems such as Jira and other BizTech enterprise applications
• Translate ambiguous requirements into clear source contracts, reusable fact and dimension models, and technical designs
• Develop configurable pipelines and shared components that simplify onboarding new 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 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

Benefits

• Paid volunteer time
• Health food and snacks
• Generous parental and family leave
• Learning and development
• Annual travel and experiences credit