Lead Data Architect

Qualifications and Competencies

Required:

• Bachelor’s Degree in computer science, information science, knowledge engineering, computational linguistics, data engineering, information technology, or a related field
• 5+ years of experience in data architecture, or a similar role, with proven leadership in designing and delivering production-grade data pipelines and services
• Strong ownership of deliverables, high standards for code review and peer mentorship, and a commitment to clear documentation, metadata/lineage publishing, and sustainable data engineering standards and practices
• Proficiency in designing and implementing data models (conceptual, logical, and physical)
• Proficiency with Databricks, including DBX Asset Bundles, Lakeflow Jobs, Unity Catalog, Access control and governance model and Databricks CLI
• Extensive experience with API design patterns and Ability to integrate data from source systems using various interface patterns, such as OpenSharing/Delta Sharing, Iceberg-Sharing, ODBC, GraphQL, OData, REST, or event streaming
• Strong engineering skills (data engineering, software engineering, or cloud engineering)
• Proficiency in Python programming, especially PySpark and Kapitan
• Proficiency in Git, including Github Actions, CI/CD Pipelines (declarative YAML), Branching strategies, and trunk-based development
• Hands-on experience with Infrastructure-as-code, ideally Terraform on AWS
• In-depth knowledge of FAIR Data Principles, Linked Data principles, and Data Mesh architecture concepts, with demonstrated ability to apply them in practice
• Proficiency with data governance and security principles and practices
• Strong analytical and communication skills
• Good communication and collaboration abilities to work independently in a self-organized team
• High level of accuracy and attention to detail

Preferred

• Experience with GxP validation and Computerized Systems Validation
• Experience in Requirements Engineering
• Experience with semantic layer concepts in modern data platforms such as Databricks, Snowflake, or Microsoft Fabric
• Experience designing and implementing Model Context Protocol (MCP) servers
• Hands-on experience with GraphRAG solution design and implementation
• Knowledge of data privacy regulations (e.g., GDPR, CCPA)
• Cloud engineering experience (e.g. AWS, Azure)
• Knowledge in the pharmaceutical domain (e.g. LOINC, SNOMED CT, omics, targets, assays, clinical studies, RWE, etc.)

As a Lead Data Architect, you are part of Bayer's Data & AI Team with a focus on creating data solutions that provide timely, high-quality data to scientists, analysts, and AI agents. You will lead the technical design and implementation of GxP-validated data solutions for clinical studies in our Clinical Data Lakehouse, combining a solid understanding for the clinical data flow with excellent technical and conceptual skills. ,[Lead the design and implementation of fit-for-purpose data solutions to store, transform, share and analyze clinical data, enabling an efficient clinical dataflow in compliance to legal and regulatory requirements, including appropriate measures to protect secret data in randomized double-blinded clinical studies in accordance to GxP. , Collaborate closely with relevant stakeholders in an agile setup to capture, specify and address data-related requirements and dependencies to ensure a fit-for-purpose technical implementation in a healthy data ecosystem. , Actively exchange with peers, embracing and applying best-practices to maintain a coherent, consolidated and scalable data landscape. , Lead the design and implementation of integration patterns, FAIR data principles and semantics to enable efficient, compliant, and secure data consumption by humans, systems, and AI agents within and beyond the primary data context. , Lead the collaboration with infrastructure experts to ensure a robust, efficient, secure and fit-for-purpose setup, leveraging infrastructure-as-code and composable architecture. , Mentor and coach less-experienced colleagues and uphold high standards in requirements specification, engineering practices, code review, testing, and documentation. , Actively exchange with other data-related initiatives across Pharma to maintain a comprehensive view of the data landscape and keep strategic alignment on data architecture. ] Requirements: Databricks