2 days ago
Responsibilities
- Develop, implement, build, and maintain production knowledge graph solutions across LPG and RDF graph technologies.
- Build scalable pipelines that transform healthcare and FHIR data into graph representations.
- Implement mappings between patient or clinical data and clinical ontology concepts.
- Develop reusable mapping and transformation patterns for graph solutions.
- Implement entity identity, canonical identifiers, URI/IRI, relationships, and concept-linking approaches.
- 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 for unresolved mappings, inconsistent relationships, and graph-quality issues.
- Collaborate with ontology, FHIR, terminology, and clinical subject-matter experts to clarify mapping requirements.
- Participate in technical design, architecture reviews, code reviews, and engineering best practices.
Requirements
- 5+ years of software or data engineering experience, including hands-on knowledge graph or graph technology experience.
- Strong understanding of knowledge graph and graph data modeling concepts.
- Hands-on experience with Labeled Property Graphs such as Neo4j/Cypher or equivalent, or RDF/semantic technologies such as RDF/RDFS, OWL, and SPARQL.
- Working knowledge of 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 with evolving requirements and cross-functional technical and domain teams.
- Preferred qualifications include hands-on experience with both LPG and RDF/semantic knowledge graphs, healthcare or life sciences data experience, and familiarity with HL7 FHIR.
- Preferred qualifications include experience with ontology-driven data integration, semantic mapping, SHACL or other graph validation approaches, terminology normalization, and canonical concept mapping.
- Familiarity with SNOMED CT, LOINC, RxNorm, ICD, or UMLS is preferred.
- Cloud and CI/CD engineering experience is preferred.
Benefits
- Full-time US-remote employment.
- Up to 25% travel to client sites across the US.
- Opportunities to upskill and work on AI, ML, data, cloud, and other emerging technologies.
- Exposure to Fortune 1000 clients and a research-focused organization with 60+ patents filed.
About Quantiphi
Quantiphi is an AI-first digital engineering and consulting firm that designs and implements machine learning, data, and cloud solutions for large enterprises across industries. Its teams build production systems—such as generative AI applications, computer vision, and predictive analytics—primarily on Google Cloud and other hyperscalers, delivered as professional services and managed solutions. Founded in 2013 and headquartered in Marlborough, Massachusetts, the company is privately held and a Google Cloud partner.