Core ValuesAt RCH, our Core Values are more than just words—they represent the threads that weave together the fabric of our culture. Used as a guide when interviewing new team members; as a barometer when evaluating our performance as individuals and teams, and even when deciding which customers to work with, RCH’s Values embody the behaviors upon which we measure our success and create a framework for our growth as people and professionals.Our Core Values:Embrace Excellence: We strive for best-in-class delivery of innovation and service.Be Accountable: Integrity, ownership and accountability are non-negotiables.Adventure Together: We are committed to fostering a culture that embraces continuous improvement.Succeed as a Team: We believe harnessing the power of a team drives outcomes not achievable by individuals.Boundaries and Balance: Work‑life balance is a core facet of our culture.If you share in our core values, then we encourage you to continue reading this posting as you may have found a great home for your career.About the profileRCH Solutions is looking for aData Engineer specialised in knowledge graphs and semantic technologiesto 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 cantake ownership of a semantic layer end to end— shaping the approach with clients and colleagues, not just implementing a specification handed to them. At RCH, you’ll buildknowledge graphs in Stardogthat 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 thesemantic backboneto modern data mesh and data fabric architectures.ResponsibilitiesDesign, build and evolveknowledge graphs in Stardog, from conceptual model through to production deployment.Model domain ontologies, taxonomies and vocabularies usingRDF, RDFS, OWLandSKOS,



and enforce them withSHACLconstraints.Write, optimise and troubleshootSPARQLqueries, rules and inference over large graphs.Integrate heterogeneous sources into the graph usingvirtual graphs and mappings(R2RML and similar) from relational databases, APIs, files and semi‑structured data.Rundiscovery sessions with subject matter experts, turning business questions into competency questions and a defensible semantic model.Align internal models withlife science standards and public ontologies, and manage identifier mapping and entity resolution across sources.Automate graph builds, tests and deployments throughCI/CD pipelinesand Python tooling.Embeddata quality, validationandreconciliation checksinto 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.Essential QualificationsHands‑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.Practicalontology and taxonomy modelling- able to move from stakeholder conversations and messy source data to a model that holds up in production.Experiencemapping and virtualisingrelational and semi‑structured sources into a graph.SolidPythonandSQLfor data preparation, transformation,



automation and troubleshooting.Comfortable withGit‑based workflows and CI/CD(GitHub Actions or Azure DevOps).Experience working withlife 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 ofdata quality, validation frameworks, and test‑driven data development.Team‑first mindset and experience inagile environments(Scrum or Kanban).Preferred QualificationsFamiliarity 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).LanguagesProfessional working proficiency in English (our internal and client‑facing working language)Local language skills (Spanish/Catalan depending on location) are plus.What we offerHybrid work model and adaptable working schedule that would suit night owls and early birds25 holiday days per yearFree English classesPossibilities of career development and the opportunity to shape the company futureAn employee‑centric culture directly inspired by employee feedback. Your voice is heard, and your perspectives encouragedDifferent training programs to support your personal and professional developmentWork in a fast growing, international companyFriendly atmosphere and supportive Management team#J-18808-Ljbffr

📌 Data Engineer - Knowledge Graphs & Semantic Technologies (Barcelona)
🏢 RCH Solutions
📍 Barcelona