Senior Knowledge Graph Engineer - Remote (US)

Quantiphi; Oregon; Hybrid; USD 120,000 - 190,000; Full time

Job summary

Quantiphi is seeking a Senior Knowledge Graph Engineer to design, build, and productionize knowledge graph solutions for healthcare data. You will work with LPG and RDF graph technologies, translating ontology and mapping specifications into scalable data pipelines and robust implementations.

You will collaborate with ontology, terminology, FHIR, and data engineering teams to deliver high-quality graph solutions and ensure data provenance and quality across systems.

Qualifications

• 5+ years of software/data engineering experience, including hands-on knowledge graphs or graph technologies.
• Strong understanding of knowledge graph and graph data modeling concepts.
• Willingness to work across LPG and RDF graph models and across teams.

Responsibilities

• Develop, implement, build, and maintain production knowledge graph solutions across LPG and RDF graph technologies.
• Build scalable pipelines to transform healthcare/FHIR data into graph representations.
• Implement mappings between patient/clinical data and clinical ontology concepts.
• Develop reusable mapping and transformation patterns for production use.
• Maintain provenance and traceability between source data, mappings, and graph entities.
• Develop and optimize graph queries using Cypher and/or SPARQL.
• Implement automated validation to identify unresolved mappings and data quality issues.

Skills

Python

Java

Knowledge graphs

Tools

Neo4j/Cypher

RDF/OWL

SPARQL

Job description

Quantiphi is seeking a Senior Knowledge Graph Engineer to design, build, and productionize knowledge graph solutions for healthcare data. You will work with LPG and RDF graph technologies, translating ontology and mapping specifications into scalable data pipelines and robust implementations.

You will collaborate with ontology, terminology, FHIR, and data engineering teams to deliver high-quality graph solutions and ensure data provenance and quality across systems.