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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and maintaining knowledge graph solutions, with strong capabilities in data transformation, mapping, and validation within healthcare contexts. Proficient in utilizing graph technologies and programming languages to ensure data quality and effective integration.
Highest-signal resume keywords
Knowledge Graph DevelopmentGraph Data ModelingPython ProgrammingNeo4j/Cypher ExperienceHealthcare Data Integration
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- 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
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 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
- Opportunities to work with Fortune 1000 companies
- Work in a research-focused organization with 60+ patents filed
- Collaboration with talented colleagues
- Remote work arrangement
- Up to 25% travel to client sites across the US
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 ManagementMapping SpecificationsData Quality AssuranceProduction Data PipelinesSemantic MappingGraph Validation
Soft Skills
Cross-Functional CollaborationAdaptability to Evolving Requirements
Tools & Technologies
Neo4jCypherSPARQLRDFFHIRSHACLCloud TechnologiesCI/CD
Industry Keywords
Healthcare DataClinical OntologyTerminology NormalizationSNOMED CTLOINCRxNormICDUMLS