Lead Data Steward

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where Health for all, Hunger for none is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us.

If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

Lead Data Steward

As lead data steward, you will be a part of Bayer's Pharma Data & AI Team. You will contribute to shaping the future of data management and governance within our organization. Your expertise will be pivotal in ensuring the integrity and effectiveness of our commercial data products, working with the data product owners to execute data management standards throughout the development and operations lifecycle.

This role involves technical skills, as well as a deep understanding of data quality and governance. Alongside with hands‑on technical data steward work, you will have additional focus on defining the stewardship processes and building the stewardship best practice and discipline within product teams.

• Collaborate closely with data product teams and the Dev Ops chapter to define and document stewardship-related processes and practices, including data quality monitoring, operational support, and cataloguing. Develop and continuously improve best practices and guidelines for data stewardship within product teams.
• Ensure adherence to data governance policies and compliance requirements across data product teams.
• Provide detailed requirements for essential tooling such as observability and quality monitoring solutions, and access management systems to technical and architecture teams.
• Mentor and support technical data stewards to ensure the effective execution of their responsibilities, maintaining high standards of quality and adherence to established processes.
• Develop and maintain a comprehensive Business Glossary within the Data Catalog.
• For your assigned data product(s) you will:
  • Design, develop, and maintain automated testing and data quality frameworks based on product requirements, i.e. by:
    • authoring test plans and scripts
    • implementing checks using e.g. dbt, Great Expectations, Py Test
    • performing regression/performance/UAT, and documenting defects in bug tracking tools
    • participating in code reviews to uphold testing and data management standards and advising on best practices.
  • Operationalize data quality in delivery pipelines by:
    • embedding quality gates in CI/CD and orchestration tools
    • collaborating with engineering to implement schema validation, contract testing, and change management for batch and streaming interfaces
    • managing quality metrics and thresholds
    • maintaining incident runbooks
    • partnering with Dev Ops teams to quickly resolve data quality issues
• Monitor and report on data product and pipeline health by:
  • using observability tools (e.g., Dagster) to detect and address issues
  • publishing quality dashboards and trend reports on data usage and data quality
• Define, steward, and maintain business and technical metadata aligned with the data catalog, glossary, and ownership model; contribute to logical and physical data models and update the catalog with lineage, asset relationships, and product certification status.
• Oversee data classification and PII tagging in compliance with data privacy regulations, in partnership with security and compliance teams.

• Bachelor’s degree in Computer Science, Information Systems, Data Science, or a related field
• 5+ years of experience in Data Stewardship, Quality Engineering, Data Management, or Data Governance roles, ideally within pharmaceutical or regulated industries, with demonstrated expertise in establishing data stewardship practices—spanning data quality monitoring, cataloging, and documentation across diverse environments.
• Good understanding of data classification, PII tagging, and compliance with…