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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing knowledge graph solutions for healthcare data, with strong capabilities in graph data modeling, data transformation, and integration. Proficient in utilizing graph technologies and programming languages to optimize data quality and maintain traceability.
Highest-signal resume keywords
Knowledge Graph DevelopmentGraph Data ModelingPython ProgrammingNeo4j/Cypher ExperienceHealthcare Data Expertise
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Knowledge Graph SolutionsGraph TechnologiesData TransformationGraph QueriesEntity Identity MappingProduction Data PipelinesRDF/Semantic TechnologiesAutomated ValidationData Quality AssuranceGraph Relationships
Soft Skills
CollaborationProblem-SolvingAdaptabilityCommunication
Tools & Technologies
Neo4jCypherSPARQLHL7 FHIRSHACLCloud TechnologiesCI/CD
Industry Keywords
Healthcare DataClinical OntologyFHIRSNOMED CTLOINCRxNormICDUMLSOntology-Driven IntegrationSemantic Mapping
Tech Stack
Tools & technologiesCloudJavaNeo4jPython
About the role
Key responsibilities & impact- Design, build, and productionize knowledge graph solutions for healthcare and clinical data
- 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
- Implement 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 graph-quality issues
- Collaborate with ontology, terminology, FHIR, clinical/domain, and data engineering teams
- Clarify ambiguous or missing mapping requirements with subject matter experts
- Participate in technical design, architecture reviews, code reviews, and engineering best practices
Requirements
What you’ll need- 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
- Hands-on experience with both LPG and RDF/semantic knowledge graphs
- Healthcare or life sciences data experience
- Familiarity with HL7 FHIR, including 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
Benefits
Comp & perks- Upskilling and professional development opportunities
- Exposure to AI, ML, data, and cloud technologies
- Opportunity to work with Fortune 1000 companies
- Research-focused work environment
- Opportunity to work alongside passionate, talented colleagues
- Opportunity to work with disruptive innovators
- Remote work arrangement
- Travel opportunities to client sites across the US