Staff Data Platform Engineer

Join KAYAK as a Staff Data Platform Engineer, where you will shape the architecture of our shared Data Platform and drive high-impact platform initiatives. You will collaborate closely with various teams, establish reliable observability, and coach and mentor engineers. Enjoy flexible work options, team-wide time off, and opportunities for career growth.

- Design and evolve the architecture of KAYAK’s shared Data Platform, including near-real-time streaming, lakehouse storage, schema management, semantic layer, and distributed query infrastructure. - Deliver high-impact platform initiatives end-to-end — from problem framing and architecture design through implementation, rollout, and operational handoff. - Define and promote technical standards for data contracts, schema evolution, ingestion patterns, and production readiness across platform and domain teams.

- Experience deploying and operating data workloads on Kubernetes — including managing containerized infrastructure, resource tuning and health checks - Experience mentoring engineers and raising technical standards through reviews, documentation, and reusable patterns - Experience building and operating streaming data pipelines — including event-driven ingestion, exactly-once delivery semantics, consumer lag management, checkpoint and recovery strategies, and failure handling in production environments - Proven ability to influence multiple teams, communicate architectural trade-offs, and drive adoption - Experience designing and operating lakehouse architectures at scale — including open table formats (e.g., Apache Iceberg), columnar storage (Parquet) and cloud object storage - Hands-on experience with data contracts, schema governance, metadata, or semantic-layer systems - Comfort taking ownership of broad, ambiguous problem spaces - 7+ years of professional experience in data engineering, with meaningful time spent at a senior or staff level with domain-wide technical scope - Strong Python skills and a track record of writing maintainable, testable production code - Distributed query engines such as Trino - Workflow orchestration tools such as Apache Airflow - CI/CD and deployment automation (e.g., GitHub Actions) - Experience with AWS or an equivalent public cloud provider - Working knowledge of Java or another JVM-based language, given the JVM-based nature of several frameworks in this domain - If you enjoy turning ambiguity into clear technical direction and durable solutions, we’d love to hear from you