Knowledge Graph Engineer
About The Position
We discover drug targets for aging and age-related disease by combining longitudinal human data with causal evidence from genetics and animal models, structured ontologies and curated external knowledge. A knowledge graph harmonizes these disparate sources to enable scientific discovery of targets, indications and mechanism of action. We are seeking a Knowledge Graph Engineer with hands-on experience in data engineering and graph technologies to design, build, and maintain scalable knowledge graph solutions. You will translate ontologies and semantic models into production systems, develop pipelines that connect enterprise data to graph platforms, and build queries that support downstream analytics and intelligent applications. This role is ideal for someone who combines strong software engineering fundamentals with an understanding of graph modeling and semantic data. You will own solutions from architecture and technology selection through implementation, testing, and performance optimization, working closely with data engineers, AI teams, and stakeholders to turn business requirements into reliable systems.
Requirements
- Bachelor’s degree or higher in computer science, data engineering, AI or a related field, with 3+ years of industry experience in data engineering.
- Experience working with biological or biomedical data, such as genomics, proteomics, clinical or phenotype data, and familiarity with standard biomedical ontologies and identifiers (e.g. UniProt, Gene Ontology, EFO, MONDO).
- Hands-on experience with graph databases such as Neo4j, Stardog or GraphDB, including writing and optimizing queries in Cypher or SPARQL.
- Working knowledge of semantic modelling concepts and standards such as RDF and OWL, and the ability to turn an ontology into a working schema.
- Experience designing and building APIs (e.g. REST or GraphQL, using frameworks such as FastAPI) that give applications and users access to graph data.
- Experience building data-quality or validation frameworks.
- Familiarity with Google Cloud and BigQuery.
- Strong Python and working knowledge of JavaScript for building data pipelines, graph integrations, and production applications.
- Strong software engineering practices, including data modeling, system design, testing, documentation and version control.
- Strong problem-solving skills and sound technical judgment when requirements are still evolving or design priorities compete.
- Ability to take ownership of a solution from initial design through implementation and ongoing maintenance.
- Clear written and verbal communication, including the ability to explain architecture decisions and technical trade-offs to clients and stakeholders.
- Ability to work effectively across data engineering, biologists and AI teams.
Nice To Haves
- Experience with LLM or agent systems
- Experience with Open Targets and other drug-discovery knowledge bases.
Responsibilities
- Maintain and extend our knowledge graph, including its schema, ingestion pipelines and query layer (Neo4j, Cypher, Apache Jena Fuseki, BigQuery on Google Cloud).
- Build and run validation and data-governance pipelines to maintain data quality, consistency, and performance. This covers schema and constraint checks, identifier reconciliation, provenance and versioning.
- Maintain our ontologies and semantic models with the biology and data science teams, and develop the mapping tool scientists use to annotate new terms and analyses. This includes the validation that keeps their mappings consistent with the graph.
- Integrate knowledge graph solutions with cloud platforms and enterprise data ecosystems, including Google Cloud and BigQuery.
- Collaborate with data engineers, data science, AI and biology teams to make graph data accessible and useful for downstream applications.
- Write clean, testable, production-grade code and apply engineering best practices throughout development and delivery.
Benefits
- competitive salary
- comprehensive compensation package
- generous paid time off
- company-observed holidays
- comprehensive health and wellness benefits (medical, dental, and vision insurance)
- 401(k) retirement savings plan with matching employer contributions
- childcare benefits
- fertility benefits
- opportunities for career development
- generous annual budget for continued learning
- dedication to training and skill development
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
