Ontology Engineer - Insurance; m​/f​/d - Inklusiver

The Risk Intelligence and Knowledge Systems (RIKS) unit is part of Life & Health division, which serves as a regional analytics centre for Europe, UK and Latin Americas. The department is responsible for AI strategy development and execution, biometrics insights and knowledge modelling for more than 40 markets, for 500+ cedents, and 1,500+ active reinsurance contracts. We strategically invest in scalable, easy-to-use data and knowledge technologies to create and operationalize high quality products and services using structured and unstructured data, serving both internal and external clients.

We believe that better data and advanced analytics can strengthen our reinsurance core and service business and will be the foundation for the business models of tomorrow.

Do you want to play a key role in shaping the future of our team? Are you the next knowledge systems champion who changes the game for us? We are looking for a motivated individual to join our team as Knowledge and Ontology Engineer. This person will design, develop, and operationalize graph-based knowledge systems, ontologies, and semantic data layers that enable intelligent data integration, discovery, and consumption across our division's diverse data landscape.

Join our team of industry experts and contribute your knowledge engineering expertise to make a significant impact on the life and health reinsurance sector.

Your Job

• Design, develop, and maintain enterprise-scale ontologies and knowledge graphs that model complex relationships across mortality, morbidity, medical conditions, reinsurance contracts, and business processes.
• Build and operationalize graph-based abstraction layers integrating diverse data sources (structured, unstructured, legacy systems) into unified semantic models accessible across business units.
• Collaborate with domain experts in underwriting, medical, claims, biometrics, and pricing to elicit, formalize, and encode domain knowledge into machine-readable ontological structures.
• Implement knowledge graph infrastructure using technologies such as Neo4j, RDF/OWL, SPARQL, or property graph databases; ensure scalability, performance, and reliability in production environments.
• Develop semantic data pipelines and ETL processes that populate, update, and maintain knowledge graphs from operational systems; ensure data quality, consistency, and lineage tracking.
• Enable self-service knowledge consumption by building APIs, query interfaces, and visualization tools that allow business users and analytics teams to explore and leverage knowledge graph capabilities.
• Partner with AI/ML teams to enhance models with structured knowledge representations, enabling explainable AI, reasoning capabilities, and domain-aware predictions.
• Design and implement knowledge governance frameworks covering ontology versioning, change management, metadata standards, and semantic interoperability across systems.
• Drive standardization and harmonization of terminology, taxonomies, and data definitions across regions and business units; establish controlled vocabularies and reference data models.
• Contribute to innovation by exploring emerging semantic technologies (knowledge graph embeddings, neural-symbolic AI, semantic search) and evaluating their applicability to reinsurance challenges.
• Document ontologies, data models, and system architectures; enable knowledge transfer through training, workshops, and best practice sharing.

Your Profile

• Preferred understanding of (re) insurance business, with experience in knowledge modelling, data architecture, or business intelligence in insurance or financial services
• Advanced degree in computer science, information science, knowledge engineering, computational linguistics, or similar
• Several years of hands-on experience designing and implementing ontologies, knowledge graphs, or semantic systems in production environments
• Deep expertise in ontology languages and standards (OWL, RDF, RDFS, SKOS), semantic web technologies, and graph database platforms (Neo4j, RDF stores, or comparable)
• Proficient in graph query languages (SPARQL, Cypher) and knowledge representation formal isms; experience with ontology engineering tools (Protégé, Top Braid, or similar)
• Strong programming skills in Python or Java with experience building knowledge graph pipelines, APIs, and integration layers
• Solid understanding of data modeling, ETL/ELT processes, SQL, and data warehouse architectures
• Experience with graph visualization, semantic search, and knowledge discovery tools and techniques
• Familiarity with linked data principles, semantic interoperability, and metadata management standards
• Skilled in collaborating with business stakeholders to translate domain expertise into formal knowledge representations
• Highly motivated, responsible, and able to work independently while driving consensus across diverse teams
• Familiar with data governance, data privacy, and regulatory frameworks in (re) insurance
• Creative problem-solver with passion for structuring complex…