Senior Knowledge Engineer

Qualifications

Required:

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, Knowledge Engineering, Computational Linguistics, or a 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 closely 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.

Preferred

• Experience with graph database technologies such as Neo4j, Stardog, GraphDB, Amazon Neptune, TigerGraph, or similar platforms.
• 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 comparable 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.

For Digital Hub Warsaw, we are looking for:

Senior Knowledge Engineer

As a Senior Knowledge Engineer, you will join Bayer's Data & AI team and help build data products and knowledge-driven solutions that enable analytics, AI, and scientific innovation across the organization.

You will work at the intersection of Data Architecture, Knowledge Engineering, and AI Enablement, helping to create scalable, AI-ready data foundations that support business users, scientists, analysts, and intelligent applications.

You will collaborate closely with domain experts, data engineers, data scientists, and product teams to model knowledge, connect data across systems, and design semantic and graph-based solutions that unlock the value of enterprise data.

What We Are Looking For

We welcome candidates from diverse backgrounds including:

• Data Architecture
• AI/Data Architecture
• Knowledge Graph Engineering
• Semantic Engineering
• Data Engineering
• Data Governance
• Platform Engineering

We value problem-solving, architectural thinking, and the ability to build scalable AI-ready data solutions over expertise in any single technology or framework.

,[Design and implement conceptual, logical, and physical data models., Develop semantic layers and knowledge representations that 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 that support 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 related to 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 that protect enterprise data, ensuring confidentiality, integrity, and availability., Collaborate with security and governance teams to ensure compliance with relevant regulations and standards.] Requirements: Python, Neo4j, Stardog, GraphDB, Databricks, Snowflake Additionally: Sport subscription, Training budget, Private healthcare, Flat structure, Small teams, International projects, Modern office, Startup atmosphere, In-house hack days, No dress code, Free parking, Free coffee, Playroom, Bike parking.