Data Engineer - Knowledge Graphs & Semantic Technologies

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…