Data Engineer, KAYAK

• 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

Desirable

• 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
• 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