Semantic Engineer

Job Description

About the role

The Semantic Engineer joins a global life sciences enterprise's knowledge graph program on TQ Data Foundation, working with the client's business and data teams and TopQuadrant. They model the client's data as governed ontologies, taxonomies and reference data, and connect them to the data platforms and AI applications where business value is realized. Success is measured on adoption, business value delivered and the client's ability to sustain and extend the work.

Responsibilities

• Use cases: rank candidates with the client by value, feasibility and data readiness, and scope each with success measures and owners.
• Modeling: build governed ontologies, taxonomies and reference data, reusing industry standards and TopQuadrant's pre-built ontologies.
• Integration: connect assets to data platforms, catalogs, graph databases and AI applications using SPARQL, JavaScript and Python.
• Governance: set up stewardship, workflows and quality rules so models stay accurate as they evolve.
• Value tracking: track the agreed success measures and report progress to program leads.
• Enablement: train and pair with client modelers until they extend the work themselves, following TopQuadrant best practices.

Required qualifications

• Experience building ontologies, taxonomies and reference data, preferably with SHACL and OWL.
• Experience with SPARQL, JavaScript and Python, including integrations with APIs and enterprise data platforms.
• Client-facing delivery experience (consulting, solutions engineering or implementation) with business and technical stakeholders.
• Degree in Computer Science, Data Science or a similar discipline, or equivalent experience.
• Strong written English and technical writing skills.
• Skilled, everyday use of AI tools.

Preferred qualifications

• Hands-on experience with TopBraid EDG or TQ Data Foundation.
• Life sciences or pharma domain experience.
• Experience grounding AI/LLM applications in knowledge graphs.

What to look for

• Measures their own work by the value the client gets.
• Thinks in classes, relationships and constraints, and can turn a messy business domain into a clean model.
• Would rather write the query or integration than describe it, and tests what they ship.
• Comfortable steering business leaders and pairing with engineers, and pushes back when a plan won't deliver value.
• Can explain a data model to a business stakeholder and a SHACL shape to an engineer.

Milestones

• 90 days: use case roadmap and success measures agreed; first modeled asset in production use.
• 6 months: one priority use case live from scoping to integrated deployment; client modelers extending the assets.
• 12 months: measurable value against the agreed measures; more use cases running on TQ Data Foundation, sustained by client teams.