Senior Knowledge Graph Engineer (Ontology)

Quantiphi; United States; On-site; USD 120,000 - 180,000; 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 lead LPG and RDF graph initiatives, translating ontology and mapping specs into scalable pipelines.

Collaborate with ontology, terminology, FHIR, and data engineering teams to implement provenance, validation, and robust querying using Cypher and SPARQL. You will shape reusable patterns and champion data quality across the platform.

Qualifications

• 5+ years of software/data engineering experience with knowledge graphs or graph technologies.
• Strong understanding of knowledge graph data modelling concepts.
• Hands-on experience with LPGs and RDF/semantic technologies.
• Proficient in Python or Java and building production data pipelines.

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 concepts in the clinical ontology.
• Develop reusable mapping and transformation patterns for production use.
• Implement consistent approaches for identity, canonical identifiers, relationships, and linking.
• Maintain provenance and traceability between source data, mapping rules, and graph entities.
• Develop and optimize graph queries using Cypher and SPARQL.
• Implement automated validation and testing to catch mapping issues and quality problems.
• Collaborate with ontology, FHIR, terminology, and clinical SMEs to clarify requirements.

Skills

Knowledge graphs

Graph data modeling

Python

Java

Production data pipelines

Cross-functional collaboration

Tools

Neo4j/Cypher

RDF/OWL/SPARQL

SHACL

Job description

We are looking for a Senior Knowledge Graph Engineer to design, build, and productionize knowledge graph solutions for healthcare and clinical data. Our architecture uses a hybrid graph approach, combining a Labeled Property Graph (LPG) for patient/clinical data with an RDF-based graph for clinical ontology and semantic knowledge. The role will focus on translating ontology and mapping specifications into scalable graph solutions, including data transformation, graph ingestion, concept/entity linking, provenance, validation, and querying. You will work closely with ontology, terminology, FHIR, clinical/domain, and data engineering teams to translate agreed semantic and mapping decisions into robust technical implementations.

Key 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 appropriate graph representations.
• Implement mappings between patient/clinical data and concepts represented in the clinical ontology.
• Develop reusable mapping and transformation patterns rather than one-off implementations.
• Implement consistent approaches for entity identity, canonical identifiers/URI/IRI, relationships, and concept linking.
• Maintain provenance and traceability between source data, mapping rules, and resulting graph entities and relationships.
• Develop and optimize graph queries using Cypher and/or SPARQL.
• Implement automated validation and testing to identify unresolved mappings, inconsistent relationships, and other graph-quality issues.
• Work with ontology, FHIR, terminology, and clinical SMEs to clarify ambiguous or missing mapping requirements.
• Participate in technical design, architecture reviews, code reviews, and engineering best practices.

Required Qualifications

• 5+ years of software/data engineering experience, including hands-on experience with knowledge graphs or graph technologies.
• Strong understanding of knowledge graph and graph data modeling concepts.
• Hands-on experience with either:
  • Labeled Property Graphs, such as Neo4j/Cypher or equivalent, or
  • RDF/semantic technologies, such as RDF/RDFS, OWL, and SPARQL.
• Working knowledge of, or ability to work across, both LPG and RDF graph models.
• Strong programming skills in Python, Java, or similar languages.
• Experience building production data ingestion, transformation, or integration pipelines.
• Experience with graph databases and/or RDF triple stores.
• Understanding of entity identity, graph relationships, mappings, provenance, and data quality.
• Ability to translate conceptual models and mapping specifications into production implementations.
• Ability to work effectively with evolving requirements and cross-functional technical/domain teams.

Other Qualifications (OQs)

• Hands-on experience with both LPG and RDF/semantic knowledge graphs.
• Healthcare or life sciences data experience.
• Familiarity with HL7 FHIR, including concepts such as Coding, CodeableConcept, Reference, and Identifier.
• Experience with ontology-driven data integration or semantic mapping.
• Experience with SHACL or other graph/semantic validation approaches.
• Experience with terminology/concept normalization and canonical concept mapping.
• Familiarity with clinical terminologies such as SNOMED CT, LOINC, RxNorm, ICD, or UMLS.
• Cloud and CI/CD experience.

What Sets You Apart?

• Hands-on experience with both LPG and RDF/semantic knowledge graphs.
• Healthcare or life sciences data experience with familiarity with HL7 FHIR and clinical terminologies (SNOMED CT, LOINC, RxNorm, ICD, or UMLS).
• Experience with ontology-driven data integration, semantic mapping, and SHACL/semantic validation approaches.
• Cloud and CI/CD engineering experience in production environment.