Senior Data Architect - Semantics & Know

As a Senior Data Architect for Semantics & Knowledge Engineering at Bayer, you are part of the Data & AI Team that co-creates domain-specific data products providing timely, high-quality data to scientists, analysts, and AI agents, emphasizing semantics as the foundation for AI-readiness. The role involves eliciting, structuring, and formalizing knowledge from domain experts and diverse sources to build ontologies, semantic layers, and knowledge graphs that integrate pharmaceutical data landscapes, enabling secondary data use, self-service analytics, and agentic AI capabilities. This position operates at the intersection of knowledge engineering and data architecture within a cloud-based environment.

• Bachelor’s Degree in computer science, information science, knowledge engineering, computational linguistics, or related field, or equivalent practical experience.
• Proficiency in designing and implementing data models (conceptual, logical, and physical).
• Advanced knowledge of ontologies and taxonomies with proficiency in Semantic Web technologies, standards, and tools (e.g., RDF, OWL, SHACL, SPARQL, TopBraid EDG).
• Proficiency with graph databases covering triple stores and labeled-property graph systems, including virtual knowledge graph technologies such as Ontop or Stardog.
• Extensive experience with API design patterns such as REST and GraphQL.
• Strong engineering skills in data engineering, software engineering, or cloud engineering.
• Proficiency in at least one programming language and basic Git version control practices.
• In-depth knowledge of FAIR Data Principles, Linked Data principles, and Data Mesh architecture concepts with practical application.
• 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 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, e.g., using GitHub Actions.
• Knowledge in the pharmaceutical domain (e.g., LOINC, SNOMED CT, omics, targets, assays, clinical studies, RWE).

• Design and implement conceptual, logical, and physical data models and semantic layers to enable consistent use.
• Design, build, and maintain ontologies, taxonomies, and knowledge graphs using triple stores and labeled-property graph technologies, including virtual knowledge graph approaches.
• Collaborate with domain experts, data scientists, data engineers, and product teams to elicit tacit knowledge, validate knowledge representations, and ensure accuracy and completeness.
• Deliver well-designed solutions to integrate, unify, and synchronize data across systems.
• Design data-oriented APIs and integration patterns that decouple data from source systems and make knowledge structures interoperable and consumable by humans, systems, and AI agents.
• Monitor emerging technologies and frameworks in knowledge engineering, data integration, and semantic AI.
• Participate actively in code reviews and collaborative engineering practices to ensure quality of deliverables.
• Drive cross-team alignment on data and knowledge architecture decisions spanning long-term ambition and near-term execution.
• Align data initiatives with business, digital, and IT strategies to address needs efficiently and effectively.
• Embrace FAIR Data Principles and Linked Data standards to promote a data culture shifting from siloed thinking to networked, democratized data use.
• Design and implement appropriate measures to protect data from unauthorized access, corruption, or theft, ensuring confidentiality, integrity, and availability of data to maintain trust and prevent risks.