Senior Knowledge Graph Engineer
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As Senior Knowledge Graph Engineer, you will operationalize domain ontologies into high-throughput graph systems powering autonomous AI agents for sustainability challenges. You’ll bridge unstructured disclosures and structured graphs, building scalable pipelines and entity resolution to create enterprise-ready data. You’ll collaborate across AI and data teams to enable real-time insights in decarbonisation, sustainable procurement, and supply chain resilience. This role offers hands-on impact at the AI Center of Excellence shaping how EcoVadis uses AI for global sustainability.
Compensaciones / Beneficios- Remote work from Spain
- Flexible working hours
- Wellness allowance
- Mental health support
- Learning and development
- Private Health Insurance
- Design, implement, and maintain high-speed GraphRAG ingestion pipelines for relational, unstructured, and streaming data into labeled property graphs and RDF stores
- Develop automated NER, linking, and deduplication workflows to resolve vendor profiles, SKUs, and coordinates into canonical graph nodes
- Enable semantic federation by ETL/ELT pipelines to map internal data with external ontologies and registries (GLEIF, W3C SSN/SOSA, Copernicus, PROV-O)
- Collaborate to build low-latency GraphRAG retrieval layers, write optimized Cypher and SPARQL queries, and support NL2Query for agents
- Operationalize SHACL shapes in CI/CD data quality tests to prevent non-compliant data mutations
- Optimize multi-hop query performance, partitioning, and indexing for sub-second traversal over billions of nodes and edges
- Degree in Computer Science, Mathematics, Engineering, or related technical discipline
- 4+ years of production experience with graph databases (Neo4j, Memgraph, TigerGraph) or RDF stores (GraphDB, Stardog, Virtuoso)
- Strong cloud experience, preferably Azure ecosystem
- Advanced Python (RDFLib, NetworkX, PyGraphistry)
- Experience with NLP frameworks for entity extraction (LangChain, LlamaIndex, spaCy) or LLM-based extraction
- Experience with dbt and integrating graphs with vector stores (Qdrant, Pinecone, pgvector) for hybrid search
- Solid knowledge of semantic web standards (RDF, RDFS, OWL, SKOS, SHACL, SPARQL) and data modeling (RML, R2RML)
- Experience with domain-specific supply chain, carbon accounting (GHG Protocol), or lifecycle data is a plus
- Experience building MCP servers to expose graph tools to LLM agents is a plus
- Experience with enterprise OBDA approaches at scale is a plus
- Collaborative, cross-functional communication
- Structured problem solving
- Attention to data quality and reproducibility
- Neo4j, Memgraph, TigerGraph
- GraphDB, Stardog, Virtuoso
- Cypher, SPARQL
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