SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Airbnb's Enterprise Core Datasets team within Tech Foundations builds the technical data foundations that power business decisions, product innovation, and operational efficiency across the organization. The team owns high-impact datasets spanning AI adoption and cost measurement, Unit Economics (revenue, costs, profitability), enterprise application integration (Jira and other BizTech systems), and critical company metrics like Gross Booking Value and Revenue.
As a Staff Data Engineer, you will provide hands-on technical leadership for datasets that shape decisions across Airbnb. You will lead complex initiatives from problem definition and architectural design through implementation, validation, and production operations. Your responsibilities include:
- Leading the architecture, implementation, and operation of high-impact data pipelines across AI usage/cost, Unit Economics, enterprise applications, and financial metrics
- Partnering with stakeholders to define how AI adoption, consumption, and spending are measured, building consistent data models
- Evolving Unit Economics datasets to connect revenue and diverse cost drivers, enabling teams to understand product profitability
- Building reliable ingestion and transformation pipelines for non-platform systems such as Jira and other enterprise applications
- Translating ambiguous requirements into clear source contracts, reusable fact and dimension models, and technical designs
- Developing configurable pipelines and shared components that simplify onboarding new data sources and business requirements
- Establishing automated testing, reconciliation, anomaly detection, and monitoring to protect data accuracy and availability
- Leading safe migrations and historical backfills through dependency analysis, validation, and recovery planning
- Optimizing distributed processing, storage, and query performance to improve freshness and reduce compute costs
- Owning production reliability through on-call participation, incident response, and lasting system improvements
- Shaping the technical roadmap, leading design reviews, mentoring engineers, and contributing to Airbnb's Data Engineering Paved Path
You will operate in an ecosystem with petabyte-scale datasets and work to build extensible data foundations that accommodate new AI tools, products, and enterprise systems while maintaining clear definitions, traceability, and reliable historical data. Your impact will extend across the data community through mentorship and contributions to shared data engineering tools and standards.
**Requirements:**
- 9+ years of relevant industry experience with a Bachelor's or Master's degree in Computer Science, Electrical Engineering, or 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 (APIs, enterprise applications, event data, 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
Plus factors: 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.
