Senior Knowledge Graph Engineer

EcoVadis is the leading provider of business sustainability ratings. The company analyzes data and builds sustainability scorecards that give companies actionable insights into their environmental, social, and ethical risks. EcoVadis operates with an international team of experts and powerful technology in the sustainability domain.

• 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 with cloud technology, preferably Azure and its ecosystem (e.g., Azure Foundry, Azure Bicep, AzureML, Azure Cloud Storage).
• Advanced proficiency in Python (RDFLib, NetworkX, PyGraphistry) for scalable, production-grade data pipelines.
• Experience building entity extraction pipelines with modern NLP frameworks (LangChain, LlamaIndex, spaCy) or LLM-based extraction.
• Hands-on with modern data transformation tools (dbt) and integrating graph databases with vector stores (Qdrant, Pinecone, pgvector) for hybrid search.
• Solid understanding of semantic web standards (RDF, RDFS, OWL, SKOS, SHACL, RDF-star, SPARQL), graph schema design, and mapping languages (RML, R2RML).

Nice to have:

• Experience with domain-specific supply chain, carbon accounting (GHG Protocol), or lifecycle assessment (LCA) data.
• Experience building Model Context Protocol (MCP) servers.
• Experience with enterprise OBDA approaches at scale.

• Design, implement, and maintain high-speed GraphRAG ingestion pipelines transforming relational data, unstructured ESG reports, and streaming feeds into operational labeled property graphs (e.g., Neo4j, Memgraph) and RDF triple stores.
• Build automated Named Entity Recognition (NER), entity linking, and deduplication workflows to unify mismatched vendor profiles, SKUs, and facility coordinates into canonical graph nodes.
• Implement automated ETL/ELT pipelines to map and federate internal supply chain data with external ontologies and registries (e.g., GLEIF, W3C SSN/SOSA, Copernicus, PROV-O).
• Partner with AI/ML engineers to develop low-latency GraphRAG retrieval layers, writing optimized Cypher and SPARQL queries, implementing NL2Query tools, hybrid vector-graph indexing pipelines, and Model Context Protocol (MCP) endpoints.
• Operationalize SHACL shapes into automated data quality tests within CI/CD pipelines to prevent non-compliant data mutations.
• Optimize multi-hop query performance, graph partitioning, and database indexing to achieve sub-second traversal over billions of nodes and edges.

Offer available only for candidates eligible to work and live in Poland. Location: Hybrid in Warsaw (4 days per month in the office) / Full remote from Poland. EcoVadis commits to equity, inclusion, and non-discrimination in the hiring process.