Data Platform Engineer

KAYAK, part of Booking Holdings (NASDAQ: BKNG), is a leading travel search engine. With billions of queries across our platforms, we help people find their perfect flight, stay, rental car and vacation package. We're also transforming business travel with a new corporate travel solution, KAYAK for Business.

As an employee of KAYAK, you will be part of a travel company that operates a portfolio of global metasearch brands including momondo, Cheapflights and HotelsCombined, among others. From start-up to industry leader, innovation is in our DNA and every employee has an opportunity to make their mark. Our focus is on building the best travel search engine leveraging AI and data to make it easier for everyone to experience the world.

We are looking for a Staff Data Platform Engineer to join our Data Platform team. Our team builds and operates the shared foundation that powers analytics, machine learning, business intelligence and AI-driven experiences across the entire company. As a senior individual contributor, you will shape how we move, store, govern, and serve data at scale, enabling every downstream team to build faster and with greater confidence.

If you enjoy turning ambiguity into clear technical direction and durable solutions, we’d love to hear from you.

This role will be required to work from our Berlin office 3 days per week.

  

In this role, you will:

• 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. Make thoughtful trade-offs between latency, correctness, cost, and long-term 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.

• Lead high-impact platform initiatives from problem framing and architecture design through implementation, rollout, and operational handoff.

• 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 Data Science, and Analytics to evolve the platform, build cross-functional support, and ensure the platform meets the needs of its users.