Graph Engineer

We are seeking a hands‑on Graph Engineer (primarily using Stardog) to design, build, and operate knowledge graph solutions that turn complex, siloed data into a connected, queryable asset for our life science clients. In this role, you will work closely with the Knowledge Management team and client stakeholders to translate data models and business questions into robust, production‑grade graph infrastructure.

Acting as a technical specialist internally and externally, you will build and maintain Stardog‑based knowledge graphs, integrate diverse data sources through modern data engineering pipelines, and ensure the ontologies and data models underpinning our graphs are sound, scalable, and maintainable.

You bring strong hands‑on experience with Stardog and data engineering, a solid understanding of ontology management concepts (RDF/OWL), and a pragmatic engineering mindset. Experience with Databricks, or in life science IT and regulated environments, is a plus and will be considered a strong asset.

• Design, build, deploy, and operate knowledge graphs on Stardog, including data modeling, virtual graphs, and reasoning configuration
• Create and maintain Stardog virtual graphs, connecting relational and external data sources to the knowledge graph without physical replication
• Design, write, and maintain SMS (Stardog Mapping Syntax) mappings to translate source schemas into RDF, ensuring accuracy, performance, and maintainability
• Write and optimize SPARQL queries and Stardog‑specific tooling (Stardog Studio, CLI, APIs) for data loading, validation, and troubleshooting
• Configure and maintain Stardog security, performance, and scalability settings for production workloads
• Monitor, tune, and troubleshoot graph database performance across ingestion, reasoning, and query layers

• Design and implement data pipelines to ingest, transform, and map source data (relational, document, file‑based) into RDF graph structures
• Build and maintain ETL/ELT processes, including source‑to‑target mapping definitions, data quality checks, and automated testing
• Integrate the knowledge graph with upstream and downstream systems, APIs, and analytics layers
• Apply sound software engineering practices: version control, CI/CD‑aware delivery, documentation, and code review

• Apply ontology management concepts (RDF, RDFS, OWL) to implement and maintain data models designed in collaboration with ontologists and business stakeholders
• Support ontology versioning, validation (e.g. SHACL), and alignment across data sources
• Contribute to documentation of data models, mappings, and graph architecture so others can understand, reuse, and build on the work

• Hands‑on experience with Stardog (or a comparable enterprise RDF triplestore/graph database)
• Solid understanding of Stardog virtual graphs and experience connecting external/relational data sources to a knowledge graph without full data replication
• Practical experience creating and maintaining SMS (Stardog Mapping Syntax) mappings
• Solid experience in data engineering: building and operating data pipelines, ETL/ELT, and data integration
• Good understanding of ontology management concepts, including RDF and OWL
• Comfortable working with SPARQL and graph query optimization
• Independent and reliable, able to take ownership of technical topics and drive them forward
• Pragmatic, focused on what works in real‑world production environments rather than theoretical perfection
• Well‑organized, documenting your work clearly and digitally so others can understand and reuse it
• Comfortable using AI tools, applying them thoughtfully to improve productivity without over‑reliance

• Experience with Azure Stack: ADLSv2, ADF, AKS, etc.
• Experience with Databricks (Spark‑based data engineering, Delta Lake, notebooks)
• Experience in Life Sciences IT, particularly in regulated environments (e.g. GxP, validated systems, data traceability requirements)

Mid

Fluent English written and spoken. Other languages a plus (especially Spanish)

• Hybrid…