Airbnb — платформа для бронирования жилья и впечатлений по всему миру.
Responsibilities
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 and build consistent data models.
Evolve Unit Economics datasets connecting revenue and cost drivers.
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 to simplify onboarding new data sources.
Establish automated testing, reconciliation, anomaly detection, and monitoring.
Lead safe migrations and historical backfills.
Optimize distributed processing, storage, and query performance.
Own production reliability through on-call participation and incident response.
Shape the technical roadmap, lead design reviews, and mentor engineers.
Requirements
9+ years of relevant industry experience with a Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field (or 6+ years with a PhD).
Track record of leading complex data engineering initiatives from ambiguous requirements through architecture, delivery, and production ownership.
Deep expertise in SQL and strong programming skills in Python or Scala.
Extensive experience with distributed processing technologies such as Apache Spark and large-scale analytical storage systems.
Strong data modeling skills: fact and dimension design, slowly changing dimensions, incremental processing, historical data management.
Experience integrating heterogeneous sources including APIs, enterprise applications, event data, and transactional systems.
Experience establishing data quality controls, observability, lineage, governance, and incident recovery practices.
Strong performance engineering skills.
Ability to align stakeholders across engineering and business functions.
Demonstrated technical leadership through mentorship and architectural guidance.