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EcoVadis, a leading provider of business sustainability ratings, is seeking a Senior Knowledge Graph Engineer to join its AI Center of Excellence. This is a hands-on, production-focused role responsible for operationalizing 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 the gap between unstructured sustainability disclosures and structured graph databases, building entity-resolution pipelines that make enterprise data agent-ready. Key responsibilities include:
**Graph Infrastructure and Ingestion Pipelines**: Design, implement, and maintain high-speed GraphRAG ingestion pipelines that transform relational data (ERP, SQL), unstructured ESG reports, and streaming feeds into operational Labeled Property Graphs (Neo4j, Memgraph) and RDF Triple Stores.
**A-Box Instantiation and Entity Resolution**: 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 and External Data Integration**: Implement automated ETL/ELT pipelines to map and federate internal supply chain data with external, open-source ontologies and registries (GLEIF for corporate ownership, W3C SSN/SOSA for IoT sensors, Copernicus for geo-hazard alerts, PROV-O for data provenance).
**GraphRAG and Agent Tooling**: Partner with AI/ML Engineers to build low-latency GraphRAG retrieval layers—writing optimized Cypher and SPARQL queries, implementing NL2Query tools for agents, hybrid vector-graph indexing pipelines, and Model Context Protocol (MCP) tool endpoints for autonomous LLM agents.
**Deterministic Guardrails and Pipeline Validation**: Operationalize SHACL (Shapes Constraint Language) shapes into automated data quality tests within CI/CD pipelines to prevent hallucinated or non-compliant data mutations from entering the enterprise knowledge graph.
**Performance Optimization and GraphOps**: 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 (2 days per week in Barcelona office) with the option for full remote work from Spain. Candidates must be eligible to work and live in Spain.
**Requirements**:
- Degree in Computer Science, Mathematics, Engineering, or related technical discipline
- 4+ years of production experience building and querying graph databases, specifically Labeled Property Graphs (Neo4j, Memgraph, TigerGraph) or RDF Triple Stores (GraphDB, Stardog, Virtuoso)
- Strong experience in cloud technology, preferably Azure and its ecosystem (Azure Foundry, Azure Bicep, AzureML, Azure Cloud Storage)
- Advanced proficiency in Python (RDFLib, NetworkX, PyGraphistry) for building 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 for heterogeneous data structures (RML, R2RML)
**Preferred qualifications**:
- Experience working 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 approaches at-scale
