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EcoVadis is seeking a Senior Knowledge Graph Engineer to join its AI Center of Excellence. You will operationalize formal domain ontologies into high-throughput, multi-hop knowledge graph systems that power autonomous AI agents solving complex sustainability challenges—including decarbonization, sustainable procurement compliance, and supply chain resilience.
You will bridge unstructured sustainability disclosures and structured graph databases, building entity-resolution pipelines that make enterprise data agent-ready. Key responsibilities include:
**Graph Infrastructure & Ingestion Pipelines**: Design and maintain high-speed GraphRAG ingestion pipelines transforming relational data (ERP, SQL), unstructured ESG reports, and streaming feeds into operational Labeled Property Graphs (Neo4j, Memgraph) and RDF Triple Stores.
**Entity Resolution & A-Box Instantiation**: Build automated Named Entity Recognition (NER), entity linking, and deduplication workflows to resolve mismatched vendor profiles, material SKUs, and facility coordinates into unified canonical graph nodes.
**Semantic Federation & External Data Integration**: Implement automated ETL/ELT pipelines mapping and federating internal supply chain data with external ontologies and registries (GLEIF for corporate ownership, W3C SSN/SOSA for IoT sensors, Copernicus for geo-hazard alerts, PROV-O for data provenance).
**GraphRAG & Agent Tooling**: Partner with AI/ML Engineers to build low-latency GraphRAG retrieval layers—writing optimized Cypher and SPARQL queries, implementing NL2Query tools, hybrid vector-graph indexing pipelines, and Model Context Protocol (MCP) tool endpoints for autonomous LLM agents.
**Data Quality & Validation**: Operationalize SHACL (Shapes Constraint Language) shapes into automated data quality tests within CI/CD pipelines to prevent hallucinated or non-compliant data mutations.
**Performance Optimization**: Optimize multi-hop query performance, graph partitioning, and database indexing strategies to handle sub-second traversal over billions of nodes and edges.
The role is hybrid (4 days per month in Barcelona office) with flexibility to work fully remote from Spain.
**Requirements:**
- Degree in Computer Science, Mathematics, Engineering, or related technical discipline
- 4+ years of production experience building and querying graph databases (Labeled Property Graphs: Neo4j, Memgraph, TigerGraph; or RDF Triple Stores: GraphDB, Stardog, Virtuoso)
- Strong experience with cloud technology, preferably Azure ecosystem (Azure Foundry, Azure Bicep, AzureML, Azure Cloud Storage)
- Advanced Python proficiency (RDFLib, NetworkX, PyGraphistry) for scalable, production-grade data pipelines
- Experience building entity extraction pipelines using modern NLP frameworks (LangChain, LlamaIndex, spaCy) or LLM-based structured extraction
- Hands-on experience with modern data transformation tools (dbt) and integrating graph databases with vector stores (Qdrant, Pinecone, pgvector) for hybrid search architectures
- Solid understanding of semantic web standards (RDF, RDFS, OWL, SKOS, SHACL, RDF-star, SPARQL), graph schema design principles (T-Box vs. A-Box separation), and mapping languages (RML, R2RML)
**Preferred qualifications:**
- Experience with domain-specific supply chain, carbon accounting (GHG Protocol), or lifecycle assessment (LCA) data structures
- Direct experience building Model Context Protocol (MCP) servers to expose graph tools to LLM agents
- Experience with enterprise OBDA (Ontology-Based Data Access) approaches at scale
Candidates must be eligible to work and live in Spain.
