Team Lead Engineering Commercial Data

Roche is a global healthcare company operating in the pharmaceutical, biotechnology, and regulated healthcare technology industry. The Commercial Data, Analytics and AI area supports Service, Marketing, Sales, and Commercial Operations across Roche Diagnostics’ global organization, enabling data-driven decision-making and operational excellence at scale. The area covers a wide range of commercial activities including customer engagement, sales effectiveness, service performance, pricing, installed base management, and operational optimization in a highly regulated, global environment.

• 5+ years of experience in data engineering leadership or architecture with a proven track record of building high-performing teams.
• 2+ years of experience within the pharmaceutical, biotechnology, or regulated healthcare technology industry.
• Bachelor’s or advanced degree in Computer Science or a similar field with a strong foundation in Business or IT specifications.
• Hands-on expertise with Snowflake, including data modeling, performance optimization, and secure access patterns in a cloud environment.
• Proficiency in analytics engineering using dbt, orchestration tools like Airflow, and modern CI/CD integration practices.
• Understanding of data quality and observability practices, including automated testing and lineage, to ensure reliability in an enterprise setting.
• Exhibits "VACC" (servant and creative) leadership behaviors, with the ability to navigate ambiguity and empower teams to act autonomously.

• Lead and develop high-performing data and analytics engineering teams, fostering a culture of technical excellence, accountability, and continuous professional growth.
• Scale engineering capabilities to support diverse Commercial use cases across Service, Marketing, Sales, and Operations through modern data modeling and transformation.
• Drive technical standards by establishing and enforcing best practices for dbt-based development, CI/CD, automated testing, and comprehensive documentation.
• Evolve data onboarding as a service to enable efficient, standardized, and scalable ingestion of new data sources while maintaining strict security and governance.
• Design robust pipelines that power advanced analytics and AI/ML solutions, ensuring all datasets are production-grade and "AI-ready" for downstream science teams.
• Collaborate with platform teams to align commercial data solutions with enterprise architecture, ensuring the reuse of platform capabilities and avoiding tool duplication.
• Manage team delivery on a day-to-day basis, translating complex commercial requirements into reliable analytics layers and high-impact insight products.
• Champion engineering craftsmanship by reviewing technical designs and guiding your team toward maintainable, cost-efficient, and scalable architectural decisions.