Data Engineer - Knowledge Graphs & Semantic Technologies

• Hands-on experience delivering production knowledge graph solutions with Stardog. Experience with other RDF triplestores (GraphDB, Amazon Neptune, Virtuoso, Anzo) counts as transferable if you’re ready to go deep on Stardog. 
• Strong command of semantic web standards: RDF, RDFS, OWL, SKOS, SHACL and SPARQL. 
• Practical ontology and taxonomy modelling - able to move from stakeholder conversations and messy source data to a model that holds up in production. 
• Experience mapping and virtualising relational and semi-structured sources into a graph. 
• Solid Python and SQL for data preparation, transformation, automation and troubleshooting. 
• Comfortable with Git-based workflows and CI/CD (GitHub Actions or Azure DevOps). 
• Experience working with life science or healthcare data, and comfortable with the quality and regulatory expectations that come with it. 
• Autonomy and ownership: you scope your own work, propose an approach, defend it, and bring the team along — rather than waiting for a fully specified ticket. 
• Working knowledge of data quality, validation frameworks, and test-driven data development. 
• Team-first mindset and experience in agile environments (Scrum or Kanban). 

• Familiarity with public life science ontologies and terminologies (e.g. SNOMED CT, MeSH, ChEBI, UMLS, LOINC). 
• Exposure to at least one life science domain: clinical and clinical trial data (CDISC, SDTM), R&D and drug discovery, regulatory (RIM, IDMP), or manufacturing, supply chain and quality. 
• Understanding of GxP or other healthcare data regulations. 
• Familiarity with FAIR data principles. 
• Experience combining graphs with AI — GraphRAG, vector search, or LLM-assisted ontology work. 
• Exposure to property graphs (e.g. Neo4j) and how they compare with RDF. 
• Knowledge of data lineage, catalog and governance tooling. 
• Infrastructure automation using Terraform, Bash, or PowerShell, and containers (Docker, Kubernetes).

• Professional working proficiency in English (our internal and client-facing working language)
• Local language skills (Spanish/Catalan, Georgian depending on location) are plus.

We’re looking for a Data Engineer specialised in knowledge graphs and semantic technologies to join our growing Data and AI Engineering team of professionals who thrive at the intersection of data, technology, and healthcare. This is a hands-on role for someone who can take ownership of a semantic layer end to end — shaping the approach with clients and colleagues, not just implementing a specification handed to them. 

At MIGx, you’ll build knowledge graphs in Stardog that connect fragmented life science data — across research, clinical, regulatory and operational domains — into models that people and machines can actually reason over. You’ll work alongside our data platform and AI engineers, contributing the semantic backbone to modern data mesh and data fabric architectures.

• Design, build and evolve knowledge graphs in Stardog, from conceptual model through to production deployment. 
• Model domain ontologies, taxonomies and vocabularies using RDF, RDFS, OWL and SKOS, and enforce them with SHACL constraints. 
• Write, optimise and troubleshoot SPARQL queries, rules and inference over large graphs. 
• Integrate heterogeneous sources into the graph using virtual graphs and mappings (R2RML and similar) from relational databases, APIs, files and semi-structured data. 
• Run discovery sessions with subject matter experts, turning business questions into competency questions and a defensible semantic model. 
• Align internal models with life science standards and public ontologies, and manage identifier mapping and entity resolution across sources. 
• Automate graph builds, tests and deployments through CI/CD pipelines and Python tooling. 
• Embed data quality, validation and reconciliation checks into the graph lifecycle. 
• Document models and enable others — governance, lineage, and reusable semantic assets that outlive the project. 
• Work in agile teams, contributing to standups, retrospectives, and continuous improvement.