Head of Data Engineering & Factory

Define and execute the vision, roadmap, and operating model for the Data Product Factory. Lead industrialization of scalable, production-grade data product engineering across Biomedical Research domains. Drive adoption of standardized engineering patterns, reusable blueprints, and contract-first design principles. Ensure all data products are artificial intelligence-ready with standardized interfaces, metadata, and controlled access mechanisms. Lead modernization of engineering practices using automation-first and artificial intelligence-native approaches. Oversee end-to-end lifecycle of data products including build, certification, deployment, and deprecation. Establish and scale continuous integration, continuous deployment, and development and operations capabilities across platforms. Embed governance, security, lineage, and policy-as-code into engineering workflows by design. Build and lead high-performing engineering teams, driving technical excellence and organizational capability growth. Partner with domain and platform leaders to align business priorities with engineering delivery outcomes.

Ready to shape how data powers scientific breakthroughs? As Head of Data Engineering & Factory, you will lead the vision and evolution of a cutting-edge Data Product Factory—transforming how scalable, artificial intelligence-ready data products are built, delivered, and consumed across Biomedical Research. This is a high-impact leadership role where you will combine deep technical expertise with strategic thinking to industrialize data engineering, accelerate innovation through automation and artificial intelligence, and enable a truly data-centric organization. You will work at the forefront of modern engineering practices, driving measurable outcomes while empowering teams to build reliable, high-quality data products at scale.

• Over 10 years of experience in software engineering, platform architecture, or enterprise data infrastructure

• Proven track record building and scaling enterprise data platforms or productized data systems

• Strong experience leading engineering teams within complex, matrixed organizational environments

• Deep expertise in continuous integration, continuous deployment, and automated testing practices in production environments

• Demonstrated experience embedding governance, access control, lineage, and compliance within engineering workflows

• Experience enabling artificial intelligence use cases through well-designed data products, interfaces, and metadata frameworks

• Strong ability to apply automation and artificial intelligence to improve engineering productivity and operational efficiency

• Excellent communication and stakeholder management skills with proven experience influencing senior leadership