Data Engineer – Knowledge Graphs, Semantic Technologies

🔥 2 hours ago

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

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RCH Solutions

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 1991

💼 Consulting

🏥 Healthcare

🏭 Manufacturing

Consulting • Healthcare • Manufacturing

RCH Solutions is a specialized provider of scientific computing and Bio-IT services, focusing on helping life sciences organizations streamline their research and development processes. They offer a range of consulting, managed, and professional services, including cloud computing, data management, and high-performance computing, aimed at accelerating scientific discovery and optimizing resource utilization for biotech and pharmaceutical companies.

📋 Description

• Design, build, and evolve knowledge graphs in Stardog from conceptual model through production deployment • Model domain ontologies, taxonomies, and vocabularies using RDF, RDFS, OWL, and SKOS • Enforce models with SHACL constraints • Write, optimise, and troubleshoot SPARQL queries, rules, and inference over large graphs • Integrate heterogeneous sources using virtual graphs and mappings such as R2RML from relational databases, APIs, files, and semi-structured data • Run discovery sessions with subject matter experts and translate business questions into competency questions and defensible semantic models • Align internal models with life science standards and public ontologies • 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 governance, lineage, and reusable semantic assets • Work in agile teams and contribute to standups, retrospectives, and continuous improvement • Collaborate with data platform and AI engineers to provide the semantic backbone for data mesh and data fabric architectures

🎯 Requirements

• Hands-on experience delivering production knowledge graph solutions with Stardog • Experience with RDF triplestores such as GraphDB, Amazon Neptune, Virtuoso, or Anzo is transferable if ready to go deep on Stardog • Strong command of RDF, RDFS, OWL, SKOS, SHACL, and SPARQL • Practical ontology and taxonomy modelling experience • Experience mapping and virtualising relational and semi-structured sources into a graph • Solid Python and SQL skills for data preparation, transformation, automation, and troubleshooting • Comfortable with Git-based workflows and CI/CD, including GitHub Actions or Azure DevOps • Experience working with life science or healthcare data • Ability to scope work, propose and defend an approach, and demonstrate autonomy and ownership • Working knowledge of data quality, validation frameworks, and test-driven data development • Experience in agile environments such as Scrum or Kanban • Familiarity with public life science ontologies and terminologies, including SNOMED CT, MeSH, ChEBI, UMLS, and LOINC • Exposure to at least one life science domain: clinical and clinical trial data, R&D and drug discovery, regulatory, or manufacturing, supply chain and quality • Understanding of GxP or other healthcare data regulations • Familiarity with FAIR data principles • Experience combining graphs with AI, including GraphRAG, vector search, or LLM-assisted ontology work • Exposure to property graphs such as Neo4j and comparison with RDF • Knowledge of data lineage, catalog, and governance tooling • Infrastructure automation experience using Terraform, Bash, or PowerShell • Experience with containers such as Docker and Kubernetes • Must be available to work on an East Coast (US) time schedule • Must not need sponsorship now or in the future • No third parties

🏖️ Benefits

• A competitive salary and bonus package based on experience • Comprehensive health and wellness benefits, including Medical, Dental, and Vision Insurance • Company-provided Life and Long-Term Disability Insurance • Company-sponsored 401(k) Plan • Team-focused culture • Unlimited opportunity for advancement • Remote work arrangement

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