Data Engineer - Knowledge Graphs & Semantic Technologies

Migx; Barcelona; Híbrido; EUR 60.000 - 85.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid work model

25 holidays per year

Free English classes

Career development

Growth company

Training programs

Friendly team

Descripción de la vacante

Migx in Barcelona, Spain, is seeking a Data Engineer specialized in knowledge graphs and semantic technologies to own the semantic layer end-to-end within our Data and AI Engineering team. This role is hands-on and you will shape the approach with clients and colleagues, not just implement a predefined specification.

You will build knowledge graphs in Stardog linking life science data across research, clinical, regulatory and operational domains, contributing to data mesh and data fabric

Formación

• Hands-on experience delivering production knowledge graph solutions with Stardog.
• Strong command of semantic web standards: RDF, RDFS, OWL, SKOS, SHACL and SPARQL.
• Practical ontology and taxonomy modelling from messy data to production model.
• 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 familiar with regulatory expectations.
• Autonomy and ownership: scope your own work, defend approach, bring the team along.
• Knowledge of data quality, validation frameworks, and test-driven data development.
• Team-first mindset and experience in agile environments (Scrum or Kanban).

Responsabilidades

• Design, build and evolve knowledge graphs in Stardog from conceptual model through production deployment.
• Model domain ontologies, taxonomies and vocabularies using RDF/RDFS/OWL/SKOS and 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) from relational databases, APIs, files and semi-structured data.
• Run discovery sessions with subject matter experts, turning business questions into a defensible semantic model.
• Align internal models with life science standards and public ontologies, manage identifier mapping and entity resolution.
• 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 governance, lineage, and reusable semantic assets that outlive the project.
• Work in agile teams, contributing to standups, retrospectives, and continuous improvement.

Conocimientos

Stardog KG

RDF/OWL/SKOS

SPARQL

Ontology modelling

Data integration

Python

SQL

Git CI/CD

Healthcare data

Herramientas

GraphDB

Amazon Neptune

Virtuoso

Anzo

Descripción del empleo

About the profile

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.

Responsibilities

• 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.

Requirements - Must have

What We’re Looking For

We believe diverse perspectives and backgrounds lead to better ideas. Even if you don’t meet every requirement, we’d still love to hear from you.

Core Experience & Skills

• 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).

Requirements - Nice to have

• 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).
• Local language skills (Spanish/Catalan, Georgian depending on location)

Languages

• Professional working proficiency in English (our internal and client-facing working language)
• Local languages a plus

What we offer

• Hybrid work model and flexible working schedule that would suit night owls and early birds
• 25 holiday days per year
• Free English classes
• Possibilities of career development and the opportunity to shape the company future
• An employee‑centric culture directly inspired by employee feedback. Your voice is heard, and your perspectives encouraged
• Different training programs to support your personal and professional development
• Work in a fast growing, international company
• Friendly atmosphere and supportive Management team.