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: - Work from (almost) anywhere for up to 20 days per year - Focus on mental health and well-being: - Company-paid therapy sessions through SpringHealth - Company-paid subscription to HeadSpace - Company-wide week off a year - the whole team fully recharges (and returns without a pile-up of work!) - No meeting Fridays - Paid parental leave - Paid volunteer time - Focus on your career growth: - Development Dollars - Leadership development - Access to thousands of on-demand e-learnings - Travel Discounts - Employee Resource Groups - 6 weeks paid vacation + a day off for your birthday - Free lunch 2 days per week - Pension plan contributions - Public transportation subsidies - Bike leasing program - Monthly social events, Thursday happy hours, sports teams - An awesome office in Friedrichshain, Berlin Inclusion At KAYAK, we want everyone to have the space to grow, share ideas and do great work. That’s why we’re focused on hiring the best talent from all walks of life and experiences, supporting them well and making sure no one feels like they have to fit a mold to belong here. Need any adjustments for the interview, application or on the job? No problem - just give us a heads-up. We’ve got you. #LI-AS1

Zur Position

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. - Fluency with AI-assisted workflows, leveraging modern tools to improve productivity and quality while critically evaluating their outputs and limitations. - Curiosity, humility, and a collaborative mindset, with a commitment to continuous learning, sharing knowledge, and improving together.

Bonus qualifications - Hands-on experience with technologies relevant to KAYAK’s stack, such as Kafka, Flink, Iceberg, Trino, AWS, Kubernetes, Airflow, Python, SQL, and schema-based data contracts. - Experience with feature stores or feature pipelines. - Familiarity with Agile ways of working, such as sprint planning and team ceremonies.