SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
KAYAK, part of Booking Holdings, is a leading travel search engine processing billions of queries across global metasearch brands including momondo, Cheapflights, and HotelsCombined. The company is transforming travel and business travel experiences through AI and data-driven innovation.
You will join the Data Platform team as a Staff Data Engineer, a senior individual contributor role responsible for shaping the shared data foundation that powers analytics, machine learning, business intelligence, and AI-driven experiences across the entire company. This is an opportunity to turn ambiguity into clear technical direction and durable solutions at scale.
Key Responsibilities:
- 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, making thoughtful trade-offs between latency, correctness, cost, and maintainability.
- 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.
- Develop reusable patterns and reference architectures for streaming ingestion, compaction, retention, schema governance, observability, and other recurring data engineering challenges.
- Establish reliable observability across the platform, including pipeline monitoring, consumer lag tracking, data quality checks, and alerting.
- Collaborate closely with Operations, Security, Engineering, Data Engineering, and Product to evolve the platform and ensure it meets user needs.
- Drive the semantic layer and metadata strategy supporting consistent and trusted self-service analytics and AI-driven data access.
- Evaluate technologies and approaches across streaming, storage, query, orchestration, and cloud infrastructure, balancing scalability, operational complexity, cost, and maintainability.
- Coach and mentor engineers through design reviews, code reviews, pairing, and reusable technical guidance.
- Own the most complex architectural and operational challenges, including failure recovery, schema drift, partition management, and performance degradation.
The role is based in the Berlin office with a requirement to work on-site 3 days per week.
Requirements:
- 7+ years of professional experience in data engineering, with meaningful time spent at a senior or staff level with domain-wide technical scope.
- Experience designing and operating lakehouse architectures at scale, including open table formats (e.g., Apache Iceberg), columnar storage (Parquet), and cloud object storage.
- 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.
- Hands-on experience with data contracts, schema governance, metadata, or semantic-layer systems.
- Strong Python skills and a track record of writing maintainable, testable production code.
- Experience deploying and operating data workloads on Kubernetes, including managing containerized infrastructure, resource tuning, and health checks.
- Proven ability to influence multiple teams, communicate architectural trade-offs, and drive adoption.
- Experience mentoring engineers and raising technical standards through reviews, documentation, and reusable patterns.
- Comfort taking ownership of broad, ambiguous problem spaces.
Desirable:
- Distributed query engines such as Trino.
- Workflow orchestration tools such as Apache Airflow.
- Experience with AWS or equivalent public cloud provider.
- CI/CD and deployment automation (e.g., GitHub Actions).
- Working knowledge of Java or another JVM-based language.
