Senior Data Architect

• Bachelor’s Degree in computer science, information science, knowledge engineering, computational linguistics, or a related field, or equivalent practical experience
• Proficiency in designing and implementing data models (conceptual, logical, and physical)
• Proficiency with relational and graph databases
• 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 at least one programming language, along with basic Git version control practices
• 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
• Ability to work collaboratively in a team environment
• High level of accuracy and attention to detail

Preferred:

• Experience with semantic layer concepts in modern data platforms such as Databricks, Snowflake, or Microsoft Fabric
• Familiarity with Enterprise Architecture and Domain Data Modelling
• Knowledge of ontologies and taxonomies with proficiency in Semantic Web technologies, standards, and tools (e.f. RDF, OWL, SHACL, SPARQL, TopBraid EDG)
• Familiarity with information retrieval systems such as Elasticsearch
• Familiarity with Data Governance solutions (e.g. Collibra)
• Experience designing and implementing Model Context Protocol (MCP) servers
• Hands-on experience with GraphRAG solution design and implementation
• Skills in knowledge retrieval from unstructured data sources
• Understanding of data security principles and practices
• Knowledge of data privacy regulations (e.g., GDPR, CCPA)
• Foundational cloud engineering experience (e.g. AWS, Azure)
• Familiarity with CI/CD practices, for example using GitHub Actions
• Knowledge in the pharmaceutical domain (e.g. LOINC, SNOMED CT, omics, targets, assays, clinical studies, RWE, etc.)

As a Senior Data Architect for FAIR data enablement in R&D, you will connect data across the whole pharma value chain with a focus on Research & Development - empowering scientists, analysts, and AI agents with fast and easy-to-access high-quality data. You will work closely together with scientists, knowledge engineers, and domain experts to enable FAIR data. Your work will directly enable secondary data use, self-service analytics, and agentic AI capabilities in a highly innovative scientific environment. ,[Lead the design and implementation of fit-for-purpose data solutions to integrate, unify and synchronize data across systems in R&D and beyond, enabling findability of data in compliance to legal and regulatory requirements. , 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. , Embrace FAIR Data Principles and Linked Data standards to promote a data culture that shifts from siloed thinking to networked, democratized data use that enables efficient, compliant, and secure data consumption by humans, systems, and AI agents within and beyond the primary data context. , Mentor and coach less-experienced colleagues and uphold high standards in requirements specification, engineering practices, code review, testing, and documentation. , Drive cross-team alignment on data and knowledge architecture decisions spanning both long-term ambition and near-term execution to maintain a coherent, consolidated and scalable data landscape, ensuring that data needs and compliance requirements are met within the broader Bayer data ecosystem. , Design and implement appropriate measure to protect data from unauthorized access, corruption, or theft, ultimately ensuring confidentiality, integrity, and availability of data to maintain trust in data and prevent legal, operational, regulatory and financial risks. , Active enterprise architecture thinking and alignment of data initiatives with business, digital, and IT strategies to ensure the respective needs are addressed most efficiently and effectively] Requirements: Databricks