Senior Knowledge Graph Engineer

EcoVadis

Senior Knowledge Graph Engineer joining AI Center of Excellence at EcoVadis. Building systems for sustainable development using AI and machine learning.

Posted 7/29/2026full-timeRemote • Warsaw • 🇵🇱 PolandSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing high-speed data ingestion pipelines, utilizing graph databases and advanced data transformation tools. Proficient in building automated workflows for entity extraction and ensuring data quality within CI/CD environments.

Highest-signal resume keywords

Graph Database DevelopmentData Pipeline EngineeringCloud Technology ProficiencyNLP Framework ExperienceSemantic Web Standards Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

GraphRAG Ingestion PipelinesCypher Query OptimizationSPARQL Query ImplementationPython ProgrammingETL/ELT Pipeline DevelopmentNamed Entity Recognition (NER)Data Transformation Tools (dbt)Graph Schema DesignModel Context Protocol (MCP)Entity Extraction Pipelines

Tools & Technologies

Neo4jMemgraphAzureRDFLibNetworkXLangChainSpaCyQdrantPineconePgvector

Industry Keywords

Supply Chain DataCarbon AccountingLifecycle AssessmentOpen-Source OntologiesData Quality TestingGraph PartitioningHybrid Search ArchitecturesData ProvenanceHeterogeneous Data StructuresEnterprise OBDA

Tech Stack

Tools & technologies

AzureCloudERPETLIoTNeo4jPythonSQL

About the role

Key responsibilities & impact

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
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
Implement automated ETL/ELT pipelines to map and federate internal supply chain data with external, open-source ontologies and registries (such as GLEIF for corporate ownership, W3C SSN/SOSA for IoT sensors, and Copernicus for geo-hazard alerts, PROV-O for data provenance)
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
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
Optimize multi-hop query performance, graph partitioning, and database indexing strategies to handle sub-second traversal over billions of nodes and edges

Requirements

What you’ll need

Degree in Computer Science, Mathematics, Engineering, or a 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 (e.g., Azure Foundry, Azure Bicep, AzureML and 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 and OWL, SKOS, SHACL, RDF-star, SPARQL), graph schema design principles (T-Box vs. A-Box separation), and mapping languages for dealing with heterogeneous data structures (RML, R2RML)
Experience working with domain-specific supply chain, carbon accounting (GHG Protocol), or lifecycle assessment (LCA) data structures is a plus
Direct experience building Model Context Protocol (MCP) servers to expose graph tools to LLM agents is a plus
Experience with enterprise OBDA approaches at-scale is a plus

Benefits

Comp & perks

Support with all the necessary office and IT equipment
Flexible working hours
Wellness allowance for mental and physical wellbeing
Access to professional mental health support
Referral bonus policy
Learning and development
Sustainability events and community involvement
Peer recognition program
Employee-led resource groups
Optional (fully covered or co-financed) health care and life insurance
Multisport card
Multikafeteria
Lunch card
Hybrid work organization
Remote work from abroad policy
Internet and Electricity bill allowance
Additional day for community service when volunteering