Senior Data Architect - Semantics & Know

O projekcie: Purpose As a Senior Data Architect for Semantics & Knowledge Engineering, you are part of Bayer's Data & AI Team that co-creates domain-specific data products that provide timely, high-quality data to scientists, analysts, and AI agents, with a strong emphasis on semantics as the foundation for AI-readiness. You elicit, structure, and formalize knowledge from domain experts and diverse sources to build ontologies, semantic layers, and knowledge graphs that form the connective tissue of an integrated pharmaceutical data landscape. Your work directly enables secondary data use, self-service analytics, and agentic AI capabilities. This role operates at the intersection of knowledge engineering and data architecture, combining formal knowledge representation with pragmatic, engineering-driven delivery in a cloud-based environment. Wymagania: - 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)- Advanced knowledge of ontologies and taxonomies with proficiency in Semantic Web technologies, standards, and tools (e.f. RDF, OWL, SHACL, SPARQL, TopBraid EDG)- Proficiency with graph databases covering both triple stores and labeled-property graph systems, including virtual knowledge graph technologies such as Ontop or Stardog- Extensive experience API design patterns such as REST and GraphQL- 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- 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, for example using GitHub Actions- Knowledge in the pharmaceutical domain (e.g. LOINC, SNOMED CT, omics, targets, assays, clinical studies, RWE, etc.) Codzienne zadania: - 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 both triple stores and labeled-property graph technologies, including virtual knowledge graph approaches where appropriate. - 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. - Monitoring of emerging technologies and frameworks in knowledge engineering, data integration, and semantic AI. - Active participation in code reviews and collaborative engineering practices to ensure quality of deliverables. - Drive cross-team alignment on data and knowledge architecture decisions spanning both long-term ambition and near-term execution. - Active alignment of data initiatives with business, digital, and IT strategies to ensure the respective needs are addressed most efficiently and effectively. - Embrace FAIR Data Principles and Linked Data standards to promote a data culture that shifts from siloed thinking to networked, democratized data use. - Design and implement appropriate measure to protect data fromunauthorized 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.