Data Engineer

About the role

KAYAK, part of Booking Holdings (NASDAQ: BKNG), is the world's 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 at our core and every employee has an opportunity to make their mark. Our focus is on building the best travel search engine to make it easier for everyone to experience the world.

KAYAK is looking for a Data Engineer to build the trusted data foundation behind machine learning across the company.

You’ll partner with data scientists to understand their modeling needs and what the available data can (and can’t) support. You’ll transform large-scale raw data into production-ready datasets and build the scalable pipelines that make them dependable and reusable. Working across Data Science, Data Engineering, and Data Platform, you’ll help address data gaps and bring new machine-learning use cases into production.

If you’re excited to shape how teams across KAYAK use data to build and scale machine learning, 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:

- Partner with the Data Science teams and help translate modeling needs into dataset requirements, then collaborate with Data Engineering and Data Platform to extend foundational pipelines into ML-ready datasets.

- Design scalable and efficient dataset workflows and ensure pipeline reliability through validation, monitoring, and alerting; diagnose and resolve issues.

- Ship production-ready data solutions that enable ML applications and deliver real-world impact on user experience and key business metrics.

- Identify gaps and opportunities, contributing to technical decisions and roadmap priorities that strengthen the data foundation for current and future ML use cases.

- Collaborate across Data Science, Engineering, and Data Platform, communicating technical decisions, findings, and trade-offs clearly and concisely to technical and non-technical audiences.

PLEASE APPLY IF YOU HAVE:

- Strong technical foundations in data engineering, software engineering, and systems design, backed by a track record of building and operating reliable, large-scale data pipelines. Demonstrated capability matters more than academic credentials.

- Hands-on experience developing production data pipelines, with the ability to clearly explain the problems addressed, your specific contributions, and the resulting impact.

- Ownership, sound judgment, and systems thinking, with the ability to navigate technical, reliability, cost, and risk trade-offs while working independently and recognizing when to seek guidance.