The Role
Build and deploy production knowledge graphs in Stardog using semantic web standards, ontology modeling, SPARQL, mappings, and virtual graphs. Integrate life science data from diverse sources, automate graph builds and CI/CD workflows with Python, enforce quality and validation, and support governance, lineage, and reusable semantic assets. Collaborate with subject matter experts and data and AI engineering teams in agile environments.
Summary Generated by Built In
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:
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.
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.
- 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.
- 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 depending on location) are plus.
- 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
Skills Required
- Hands-on experience delivering production knowledge graph solutions with Stardog or transferable RDF triplestore experience
- Strong command of RDF, RDFS, OWL, SKOS, SHACL, and SPARQL
- Practical ontology and taxonomy modeling experience
- Experience mapping and virtualizing relational and semi-structured data sources into graphs
- Solid Python and SQL skills
- Experience with Git-based workflows and CI/CD, including GitHub Actions or Azure DevOps
- Experience working with life science or healthcare data
- Ability to work autonomously, scope work, propose approaches, and take ownership
- Working knowledge of data quality, validation frameworks, and test-driven data development
- Experience working in agile environments such as Scrum or Kanban
- Familiarity with public life science ontologies and terminologies such as SNOMED CT, MeSH, ChEBI, UMLS, or LOINC
- Exposure to clinical, clinical trial, R&D, drug discovery, regulatory, manufacturing, supply chain, or quality domains
- Understanding of GxP or other healthcare data regulations
- Familiarity with FAIR data principles
- Experience combining knowledge graphs with AI, GraphRAG, vector search, or LLM-assisted ontology work
- Exposure to property graphs such as Neo4j
- Knowledge of data lineage, catalog, and governance tooling
- Experience with Terraform, Bash, PowerShell, Docker, or Kubernetes
- Professional working proficiency in English
- Spanish or Catalan language skills
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The Company
What We Do
RCH Solutions is a global provider of specialized scientific computing and Bio-IT services for Life Sciences and Healthcare organizations. They help clients clear the path to discovery and streamline research across the entire lifecycle, from early R&D and preclinical research to clinical operations and commercialization, utilizing expertise in cloud, HPC, AI/ML, and data science.
