Semantic Data Engineer

To qualify for the role, you must have

• Relevant experience guide: Guide / 5-10 years
• 4-10 years of experience in Data Engineering, Data Management, Information Architecture, Semantic Data Engineering or AI-ready data platform delivery.
• 2-4+ years of hands-on experience with semantic technologies, ontology implementation, knowledge graphs, graph modelling or semantic data platforms.
• Strong implementation experience with SQL, Python, PySpark, RDF, OWL, SPARQL, semantic models, metadata engineering, data pipelines and modern lakehouse architecture.
• Preferred experience with Neo4j, Amazon Neptune, GraphDB, Microsoft Purview, Unity Catalog, Immuta, Power BI Semantic Models, GraphRAG, vector search and enterprise AI solutions.

Ideally, you'll also have

• Implementation-focused engineer who can operationalise semantic architecture into reusable pipelines, semantic models, metadata assets and knowledge graph solutions.
• Strong collaboration skills across semantic architects, data architects, data engineers, governance teams, stakeholders and AI engineering teams.
• Comfortable building governed, lineage-aware, observable and AI-ready semantic data products for enterprise search, GraphRAG and self-service analytics.
• Able to document implementation patterns, support architecture decisions and improve operational reliability through DevOps, Data SRE and observability practices.
• This role is not a traditional data engineer. It is a Semantic Data Engineer who operationalises ontologies, semantic models, metadata pipelines, knowledge graphs and AI-ready data products so enterprise knowledge can be consumed reliably by analytics, search, GraphRAG and agentic AI systems.