Knowledge Graph and Semantic Governance Manager
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Job description
Here, the answers aren't always available. So, you'll need to bring a fearless, self-starter mindset to navigate uncharted territories. You'll harness your ceaseless energy to discover and make the necessary connections with colleagues to shape the future and achieve maximum impact., Global Operations Data Office leads the transformation to AI-ready data that powers Ops 2030. We enable trusted, structured, contextualized data and scalable data products - delivered responsibly. We partner across Pharmaceutical Technology & Development, Manufacturing, Global Engineering, Quality, Sustainability, Global Supply Chain, and Procurement, operating from hubs in the UK, Sweden, US, China, and Global Technology Centres in India and Mexico. Our work directly impacts patients by accelerating digital, data, and AI-driven operations., We are hiring a Knowledge Graph and Semantic Governance Manager to lead the delivery of the semantic layer strategy for Global Operations. You will:
- Set the semantic strategy and framework for building ontologies and knowledge graphs, guiding standards, platforms, and engineering practices. Understand the way the LLM's consume and reason over structured knowledge and take this into consideration of the overall sematic strategy and framework.
- Design and deliver ontology-driven models and embed semantic capabilities into data products and analytics across manufacturing, supply chain, and development. This will require close collaboration with AI/ML engineers and data scientists who builds solutions but also enterprise teams who are shaping and governing the semantic layer.
- You'll collaborate closely with Operations IT, R&D Semantic Engineering, and the Enterprise Data Office to deliver FAIR, interoperable data that scales AI and automation.
- Govern Global Operations knowledge graphs and ontologies built in alignment with the Global Operations reference model.
- Define the governance Framework for Knowledge graphs and ontologies for Global Operations.
What You'll Do
- Leadership & Strategy: Design and evolve the semantic strategy and framework for Global Operations; establish and chair/co-chair governance for Global Operations knowledge graphs; define standards, lifecycle, versioning, and stewardship; track and introduce best practices in knowledge representation. Understand how LLM's consume and reason over structured knowledge.
- Semantic Architecture & Modelling: Set direction for ontology and metadata modelling across Global Operations; oversee design of ontologies, taxonomies, and reference data; ensure alignment with the Operations Reference Data Model and enterprise architecture; guide use of RDF/OWL, SKOS, SHACL, and property graphs (e.g., Neo4j). This requires close collaboration with other teams across AZ enterprise (i.e. other Set area Data Offices, Enterprise Data Office, Enterprise Architecture, AI/ML engineers and data scientists)
- Build Ontologies: Be part of the team that build ontologies enabling the Ops 2030 use cases
- Product and Platform Enablement: Collaborate with It to embed semantic standards into data products, APIs, and integrations; partner with product teams to enable high-value use cases; influence enterprise selection and use of platforms (GraphDB, Neptune, etc.) and tools (Protégé, VocBench, Metaphactory)., Your wellbeing means a lot to us, and we're here to support you through all of life's ups and downs. That's why we offer an unpaid leave policy, annual leave, reduced-hours timetables and a host of benefits, including a retirement plan, long service award, and health and travel insurance.
Requirements
Ready to make an impact in your career? If you're passionate, growth-orientated and a true team player, we'll help you succeed. Here are some of the skills and capabilities we look for., * Deep experience leading semantic data/knowledge graph programs in complex, global environments, with delivery of production solutions.
- Proven stakeholder management skills
- Expertise in ontology engineering and semantic web standards (RDF, OWL, SKOS, SHACL, SPARQL) and familiarity with property graph modelling and Cypher.
- Strong background in metadata, taxonomy/thesaurus development, and terminology standardization.
- Proven ability to translate business capabilities into semantic models and data products; effective engagement with both technical and non-technical stakeholders.
- Solid programming/automation skills and modern engineering practices; Git/GitHub for version control.
- Bachelor's degree in computer science, Information Systems, Data Engineering, or related; Master's preferred.
Desirable
- Knowledge of FAIR principles, data governance, and enterprise data architecture.
- Hands-on experience with Neo4j/Cypher and semantic platforms (GraphDB, Amazon Neptune); tools such as Protégé, VocBench, Metaphactory.
- Exposure to manufacturing and industrial data (ERP, MES, IoT; Cognite Data Fusion).
- Experience applying graphs in AI/ML (embeddings, reasoning, LLM integration).