Senior Knowledge Engineer

Bayer is hiring for its Digital Hub Warsaw, focusing on data products and knowledge-driven solutions in the Life Sciences, Healthcare, Pharmaceutical, and Research environment.

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, Knowledge Engineering, Computational Linguistics, or related field, or equivalent practical experience.
• Strong experience in data modelling (conceptual, logical, and physical).
• Experience in Data Architecture, Knowledge Engineering, Data Engineering, Metadata Management, or related disciplines.
• Experience with knowledge graphs, graph databases, semantic technologies, or metadata-driven architectures.
• Hands-on experience designing APIs and data integration solutions.
• Strong engineering background in data engineering, software engineering, cloud engineering, or platform engineering.
• Proficiency in at least one programming language.
• Familiarity with Git and collaborative software development practices.
• Strong analytical, communication, and stakeholder management skills.
• Ability to collaborate effectively across technical and business teams.

Nice to have:

• Experience with graph database technologies such as Neo4j, Stardog, GraphDB, Amazon Neptune, or TigerGraph.
• Experience with ontology modelling, taxonomies, semantic modelling, or Semantic Web standards (e.g. RDF, OWL, SHACL, SPARQL).
• Experience with modern cloud data platforms such as Databricks, Snowflake, Microsoft Fabric, AWS, Azure, or GCP.
• Familiarity with metadata management and data governance solutions.
• Experience designing AI-ready architectures, knowledge retrieval solutions, GraphRAG, RAG, or Agentic AI applications.
• Understanding of FAIR Data Principles, Linked Data concepts, Data Mesh, or similar modern data architecture approaches.
• Familiarity with Elasticsearch or enterprise search technologies.
• Experience with CI/CD practices and DevOps tooling.
• Knowledge of data privacy, security, and regulatory requirements.
• Experience within Life Sciences, Healthcare, Pharmaceutical, or Research environments.

• Design and implement conceptual, logical, and physical data models.
• Develop semantic layers and knowledge representations to improve data discoverability and reusability.
• Build and evolve knowledge graph solutions, metadata-driven architectures, and semantic data products.
• Collaborate with domain experts to capture and formalize business knowledge into scalable data models.
• Ensure consistency, quality, and usability of enterprise data assets.
• Design scalable integration patterns across diverse data sources and platforms.
• Develop data-oriented APIs and services supporting analytical and AI-driven use cases.
• Enable interoperability between systems, applications, and intelligent agents.
• Contribute to AI-ready data architecture and semantic foundations for GenAI and Agentic AI use cases.
• Support implementation of knowledge retrieval and graph-based AI solutions.
• Evaluate emerging technologies in Data Management, Knowledge Engineering, Semantic AI, and Generative AI.
• Collaborate with engineering teams through code reviews and architecture discussions.
• Promote best practices in software development, data engineering, and platform architecture.
• Contribute to continuous improvement of engineering standards and delivery practices.
• Support enterprise-wide data governance, metadata management, and data quality initiatives.
• Drive alignment between data architecture decisions and business objectives.
• Promote modern practices related to data sharing, accessibility, and responsible data usage.
• Design solutions protecting enterprise data confidentiality, integrity, and availability.
• Collaborate with security and governance teams to ensure compliance with regulations and standards.