Core Values At 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 profile

RCH Solutions is 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 RCH, 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. Essential Qualifications

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). Preferred Qualifications

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). Languages

Professional working proficiency in English (our internal and client‑facing working language) Local language skills (Spanish/Catalan depending on location) are plus. What we offer

Hybrid work model and versátil 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

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