Semantic Product Information Architect

The Corporate PLM Office and the Center of Competence PLM at B. Braun are shaping the future product data landscape. In this strategic and technical role, you design, model and implement an Enterprise Product Knowledge Graph as a semantic information layer for the digital product lifecycle, based on RDF, RDFS and OWL, with SHACL for data quality.

• Design, build and further develop an Enterprise Product Knowledge Graph, and model and implement semantic information models and ontologies based on RDF, RDFS and OWL.
• Define information objects, classes, properties, semantic relationships, taxonomies, vocabularies, namespaces and reusable semantic patterns along the product lifecycle.
• Develop SHACL shapes and rules to validate semantic data and use SPARQL to analyze, validate and query the knowledge graph.
• Implement and maintain semantic models in a suitable knowledge graph or Semantic Web platform, and define and maintain mappings between semantic information objects and operational data structures.
• Semantically link product information with the underlying data records and systems of record, and ensure consistency and traceability between semantics, knowledge graph and operational data sources.
• Establish governance, versioning and modeling guidelines for ontologies and semantic information models.
• Work closely with PLM, Corporate Data Framework (CDF), Data Intelligence Platform (DIP), data governance and enterprise architecture, advise business departments and moderate related workshops.

• Completed degree in computer science, business informatics, engineering or a comparable field, plus several years of experience in Semantic Web, knowledge graphs, ontology engineering, information architecture, data governance or PLM.
• Practical experience with RDF, RDFS, OWL and SHACL as well as good knowledge of SPARQL and graph-based query models.
• Experience with knowledge graph or Semantic Web platforms, for example Linksphere, GraphDB, Stardog, TopBraid EDG, Protégé or comparable technologies.
• Experience modeling ontologies, classes, properties, relationships, taxonomies and metadata, and understanding of URI/IRI, namespaces and Linked Data principles.
• Experience mapping operational data structures to semantic information models; knowledge of product data structures and Product Lifecycle Management is an advantage.
• Knowledge of MBSE, systems engineering or enterprise architecture and experience in regulated industries such as medical technology, pharma or life sciences are an advantage.
• Strong conceptual and analytical thinking, structured and solution-oriented way of working, strong communication and facilitation skills, and enthusiasm for Semantic Web, knowledge graphs, data quality and AI-ready information.