Data Engineer

Lead Knowledge Graph Engineer: Work Location: London UK. Hybrid Working (12 days per month) Experience Range: 10+ yrs B2B Contract. Budget, 450- 500 GBP Per Day Primary Skill : 1. Knowledge of RDF, SPARQL, and OWL. 2. At least 1 year of hands-on experience with an RDF database such as Stardog, AnzoGraph, Blazegraph, or Apache Jena. 3. Strong understanding of ontology concepts. 4. Python. 5. Generative AI, RAG, and GraphRAG. Optional skills: 1. Neo4j, TigerGraph, or other property graph databases. 2. Experience working with biomedical datasets. 3. Knowledge of the Azure platform. Role Overview - We are seeking a Knowledge Graph Lead Engineer to evaluate, optimize, and scale our enterprise semantic data infrastructure. In this role, you will bridge the gap between complex biomedical data and actionable AI applications. You will conduct comprehensive health checks on our current stack, refine bio-ontologies, and build the strategic roadmap for our next-generation GraphRAG and GenAI platforms. Key responsibilities - • Platform Assessment: Conduct comprehensive technical health checks on existing graph databases and cluster infrastructure. • Ontology & Schema Review: Evaluate RDF/OWL and Labeled Property Graph (LPG) schemas for enterprise scalability. • Standards Alignment: Map internal data frameworks to biomedical standards like MeSH, SNOMED, and UMLS. • Performance Optimization: Eliminate data bottlenecks across real-time ingestion pipelines and complex query execution. • Strategic Roadmap: Author comprehensive "Way Forward" reports detailing cloud migration and build-vs-buy decisions. • AI & LLM Integration: Design infrastructure to connect knowledge graphs with Large Language Models using GraphRAG frameworks. • Stakeholder Alignment: Translate technical graph concepts into clear business value for Research and Clinical teams. Skills - Graph Expertise: 10+ years of engineering experience with Graph Databases, Triple Stores, or Labeled Property Graphs. Technical Stack Preferences • Graph Databases: Stardog, AnzoGraph, or Neo4j • Query & Programming Languages: SPARQL, Cypher, Gremlin, Python, and Java • AI Tools: Any Vibe Coding Tool (Claude Code OR GHCP) Pharma Domain Knowledge - • Pharma Domain Knowledge: Proven track record handling biomedical data like gene-disease associations and chemistry structures. • CMC Data Familiarity: Experience modeling Chemistry Manufacturing and Control data types, including product journeys and electronic data capture logs. • Bio-Ontologies & Datasets: OBO Foundry, ChEMBL, Ensembl, and Monarch Initiative • Advanced AI/GenAI: Hands-on experience designing and executing Graph RAGs, Context Graphs, Agents • Semantic Web Standards: Deep understanding of W3C standards, Linked Data principles, and URI minting strategies.