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Por favor, lea detenidamente la siguiente descripción del puesto para asegurarse de que encaja con el perfil antes de enviar su solicitud.
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
• 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
Responsabilidades
• 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 xqbhyrx 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
Requisitos principales
• Remote work from Spain
• Flexible working hours
• Wellness allowance
• Mental health support
• Learning and development
• Private Health Insurance
or continue with email
Por favor, lea detenidamente la siguiente descripción del puesto para asegurarse de que encaja con el perfil antes de enviar su solicitud.
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
• 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
Responsabilidades
• 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 xqbhyrx 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
Requisitos principales
• Remote work from Spain
• Flexible working hours
• Wellness allowance
• Mental health support
• Learning and development
• Private Health Insurance
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