Senior Knowledge Graph Engineer

EcoVadis; Barcelona; Híbrido; EUR 90.000 - 120.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote work from abroad policy

Meals and Transportation Vouchers (COV

Dental Benefits

Life & Accident Insurance + Private HP

Learning and development

Wellness allowance

Referral bonus policy

Descripción de la vacante

EcoVadis is seeking a hands-on, senior Knowledge Graph Engineer to operationalize ontologies into high-throughput, multi-hop graph systems and empower autonomous AI agents for sustainability challenges.

You will work with Neo4j, Memgraph, and RDF stores, building ingestion pipelines, entity resolution, and semantic federation while collaborating with AI/ML teams across Europe, with remote options. Location: Barcelona, Spain, hybrid.

Formación

• 4+ years of production experience building and querying graph databases (LP graphs or RDF stores).
• Strong experience in cloud technology, preferably Azure ecosystem.
• Advanced Python (RDFLib, NetworkX, PyGraphistry) for scalable data pipelines.
• Experience building entity extraction pipelines using NLP frameworks (LangChain, LlamaIndex, spaCy) or LLM-based extraction.
• Hands-on data transformation with dbt and integrating graph DBs with vector stores for hybrid search.
• Knowledge of RDF, RDFS, OWL, SKOS, SHACL, SPARQL; graph schema design and mapping languages.
• Experience with domain data like supply chain or carbon accounting is a plus.
• Experience with MCP servers and enterprise OBDA approaches is a plus.

Responsabilidades

• Graph Infrastructure and Ingestion Pipelines
• Design, implement, and maintain high-speed GraphRAG ingestion pipelines transforming ERP data, ESG reports, and feeds into labeled graphs and RDF stores
• A-Box Instantiation and Entity Resolution
• Build automated NER and deduplication workflows to canonicalize graph nodes
• Semantic Federation and External Data Integration
• ETL/ELT pipelines to map and federate data with external ontologies and registries
• GraphRAG and Agent Tooling
• Collaborate with AI/ML to build low-latency retrieval layers and NL2Query tools
• Deterministic Guardrails and Pipeline Validation
• Operationalize SHACL shapes into CI/CD data quality tests
• Performance Optimization and GraphOps
• Improve multi-hop query performance and indexing for billions of nodes/edges

Conocimientos

Graph databases

Python

NLP pipelines

LangChain / LlamaIndex

dbt

Cypher / SPARQL

Vector stores integration

Azure cloud

Educación

Degree in Computer Science, Mathematics, Engineering

Herramientas

Neo4j

Memgraph

TigerGraph

GraphDB

Stardog

Virtuoso

dbt

Azure

Descripción del empleo

Work smart, have fun and make an impact!

EcoVadis is the leading provider of business sustainability ratings. Our solutions are backed by an international team of experts and powerful technology. We analyze data and build sustainability scorecards that give companies actionable insights into their environmental, social and ethical risks.

Why apply to EcoVadis?

Be a part of the global sustainability change in business. Grow your career. Work with extraordinary people. Feel valued for your contribution.

Learn more about our team and culture on EcoVadis careers page.

Job Description

We are looking for a hands‑on, production-focused Senior Knowledge Graph Engineer to join our growing AI Center of Excellence, responsible for using AI and machine learning to drive innovation across the organization. In this role, you will take the formal domain ontologies designed by our Knowledge Representation Architects and operationalize them into high-throughput, multi‑hop systems. We welcome applications from European locations, with the possibility of remote work. Join us!

Your work will directly power autonomous AI agents that solve complex sustainability challenges — including decarbonisation, 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.

Your responsibilities will include (but will not be limited to):

• 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 (such as GLEIF for corporate ownership, W3C SSN/SOSA for IoT sensors, and 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

Qualifications

• 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

Additional Information

• Offer available only for candidates eligible to work and live in Spain


• Location: Hybrid in Barcelona (4 days per month in the office) / Full remote from Spain

In return for your expertise, we offer:

• 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


• Remote work from abroad policy


• Meals and Transportation Vouchers (Coverflex card)


• Dental Benefits


• Life & Accident Insurance + Private Health Insurance


• Paid employee volunteer day


• Paid moving day (1/year)


• Time off: 1 Community Service Day + 1 Personal Day


• Summer Hours in July and August (36 hours per week)


• Hybrid Monthly Allowance for electricity and Internet

We’re interested in hiring from a diverse and skilled talent pool, regardless of professional and educational background.

Can the hiring process be adjusted to suit my needs? Yes. We want everyone going through the hiring process with EcoVadis to feel confident that you are able to demonstrate your full potential. We welcome applications from disabled people, people with long‑term health conditions, and neurodiverse candidates. If you need any adjustments, including the provision of interview questions, please let the hiring team know.

Our team’s strength comes from everyone’s uniqueness and is founded upon mutual respect.

EcoVadis commits to equity, inclusion and reducing bias in our hiring processes. EcoVadis does not accept any form of discrimination based on color, national or ethnic origin, ancestry, citizenship, religion, beliefs, age, sex, gender identity, sexual orientation, neurodiversity, disability, parental status, or any other protected characteristic that makes you unique. In your application, we encourage you to remove personal information such as: photographs, marital status, number of children, religion, gender, residential postal code, university graduation date, past medical or parental leave(s) taken, nationality (instead, please state if you are legally eligible to work in the job region/country), university name (instead, please state any degrees obtained and the study major).

Our recruitment processes do not include AI systems that make autonomous decisions impacting individuals’ rights without human oversight.

Inputs and outputs of the system have been validated by humans and answers are based on algorithms that analyze previous data with which the system has been trained. We use SmartRecruiters as our applicant tracking system and to schedule interviews. Gemini is used to summarize notes from interviews and to refine job descriptions and social media messaging. AssessFirst is used to support talent assessment decisions.