Overview In this role you will operationalize domain ontologies into high-throughput graph systems powering autonomous AI agents solving sustainability challenges. You’ll bridge unstructured disclosures with structured graphs and build scalable ingestion and entity-resolution pipelines. You’ll enable semantic federation with external data and optimize performance for sub-second traversals at scale. You work closely with AI/ML engineers to drive practical, data-driven sustainability solutions. Compensaciones / Ventajas * Flexible working hours * Wellness allowance * Mental health support * Remote work from abroad policy * Dental Benefits * Life & Accident Insurance + Private Health Insurance Responsabilidades * Design and maintain high-speed GraphRAG ingestion pipelines converting ERP, SQL, unstructured ESG reports, and streaming data into labeled property graphs and RDF stores * Build automated NER, entity linking, and deduplication workflows to unify vendor profiles, SKUs, and coordinates into canonical graph nodes * Implement automated ETL/ELT pipelines to federate internal supply chain data with external ontologies and registries (GLEIF, W3C SSN/SOSA, Copernicus, PROV-O) * Develop low-latency GraphRAG retrieval layers, write Cypher and SPARQL queries, and create NL2Query tools for autonomous LLM agents * Operationalize SHACL shapes into automated data quality tests in CI/CD to prevent data mutations in the graph * Optimize multi-hop queries,



graph partitioning, and indexing for sub-second traversal over billions of nodes and edges * Collaborate with AI/ML engineers to integrate graph databases with vector stores for hybrid search architectures * Ensure alignment with enterprise OBDA approaches and scalable graph tooling Requisitos principales * Degree in Computer Science, Mathematics, Engineering, or a related technical discipline * 4+ years of production experience with graph databases (Neo4j, Memgraph, TigerGraph) or RDF stores (GraphDB, Stardog, Virtuoso) * Strong cloud experience (Azure preferred) and related tooling * Advanced Python skills (RDFLib, NetworkX, PyGraphistry) for scalable data pipelines * Experience with NLP frameworks (LangChain, LlamaIndex, spaCy) or LLM-based extraction * Hands-on with data transformation tools (dbt) and vector stores (Qdrant, Pinecone, pgvector) for hybrid search * Solid understanding of semantic web standards (RDF, RDFS, OWL, SKOS, SHACL, RDF-star, SPARQL) and mapping (RML, R2RML) * Experience with domain data in supply chain, carbon accounting (GHG Protocol), or LCA is a plus * Experience building MCP servers to expose graph tools to LLMs is a plus * Experience with enterprise OBDA approaches at scale is a plus * Collaborative * Problem-solving * Detail-oriented * Graph databases (Neo4j, Memgraph, TigerGraph) * RDF and SPARQL * Cypher

📌 Senior Knowledge Graph Engineer (Barcelona)
🏢 Ecovadis
📍 Barcelona