Vice President of Data, KAYAK

• Experience: 10+ years building data platforms and analytics/ML organizations, with at least 5 years in senior leadership
• Technical depth: Strong background in modern data engineering and data science practices (ETL/ELT, data modeling, warehousing/lakehouse, streaming, feature stores, ML lifecycle). Familiarity with cloud platforms (AWS/Azure/GCP) and CI/CD for data/ML workflows
• Leadership: Proven track record hiring, scaling, and developing technical teams and partnering across product and engineering organizations
• Governance & security: Experience implementing data governance, privacy controls, access management, and compliance processes
• Communication & business impact: Able to translate technical tradeoffs into business value and influence senior stakeholders
• Education: Degree in CS, engineering, statistics, or related field (or equivalent experience)

Desirable

• The VP, Data will own KAYAK’s data strategy and platform, leading Data Engineering and Data Science to deliver reliable, scalable, and impactful data products
• This leader will define the data roadmap, ensure governance and quality, enable analytics and ML at scale, and partner closely with DevOps, Engineering, Product, and Security teams to operationalize data solutions (without directly managing DevOps teams)
• Define and drive the company’s data strategy and roadmap to enable analytics, experimentation, and machine learning aligned to business goals
• Lead design, development, and evolution of the data platform (ingestion, processing, storage, catalogs, APIs) to ensure scalability, reliability, and performance
• Oversee data science teams to deliver predictive models, experimentation frameworks, and ML systems that create measurable business impact
• Establish and enforce data governance, metadata management, lineage, and data quality standards to ensure trust and compliance
• Partner closely with DevOps, Cloud/Infrastructure, Engineering, Product, and Security to productionize data products, define SLAs, and align on deployment and monitoring practices—collaborating with but not directly managing DevOps teams
• Recruit, mentor, and grow high-performing data engineering and data science teams; define org structure, career paths, and performance metrics
• Define KPIs for data platform adoption, model performance, data quality, and time-to-insight; use outcomes to prioritize investments
• Guide architecture choices (cloud-native data stacks, streaming vs batch, data warehouse vs data lakehouse), review critical designs, and promote best practices in engineering and ML ops