
1001 - 5000 employees
Founded 2013
đŒ Consulting
đ„ Healthcare
đŠ Logistics
đ° Series A on 2019-12
Consulting âą Healthcare âą Logistics
Quantiphi is a leading AI-first digital engineering company that leverages a decade of industry expertise to empower businesses through scalable, secure, and adaptable AI solutions. By integrating cutting-edge technology with real-world applications, Quantiphi transforms organizations across various sectors including healthcare, finance, education, and retail. Their services span AI applications, data analytics, cloud infrastructure modernization, and custom AI implementations. Quantiphi partners with technology giants like AWS, Google Cloud, NVIDIA, and others to drive AI adoption and deliver transformational opportunities for enterprises.
đ„ 0 minutes ago
đșđž United States â Remote
â° Full Time
đ Senior
đ° Data Engineer
đŠ H1B Visa Sponsor
đ» Ghost score 11%
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1001 - 5000 employees
Founded 2013
đŒ Consulting
đ„ Healthcare
đŠ Logistics
đ° Series A on 2019-12
Consulting âą Healthcare âą Logistics
Quantiphi is a leading AI-first digital engineering company that leverages a decade of industry expertise to empower businesses through scalable, secure, and adaptable AI solutions. By integrating cutting-edge technology with real-world applications, Quantiphi transforms organizations across various sectors including healthcare, finance, education, and retail. Their services span AI applications, data analytics, cloud infrastructure modernization, and custom AI implementations. Quantiphi partners with technology giants like AWS, Google Cloud, NVIDIA, and others to drive AI adoption and deliver transformational opportunities for enterprises.
âą Design, build, and productionize knowledge graph solutions for healthcare and clinical data âą Develop, implement, build, and maintain production knowledge graph solutions across LPG and RDF graph technologies âą Build scalable pipelines to transform healthcare/FHIR data into appropriate graph representations âą Implement mappings between patient/clinical data and concepts represented in the clinical ontology âą Develop reusable mapping and transformation patterns âą Implement approaches for entity identity, canonical identifiers/URI/IRI, relationships, and concept linking âą Maintain provenance and traceability between source data, mapping rules, and resulting graph entities and relationships âą Develop and optimize graph queries using Cypher and/or SPARQL âą Implement automated validation and testing to identify unresolved mappings, inconsistent relationships, and graph-quality issues âą Collaborate with ontology, terminology, FHIR, clinical/domain, and data engineering teams âą Clarify ambiguous or missing mapping requirements with subject matter experts âą Participate in technical design, architecture reviews, code reviews, and engineering best practices
âą 5+ years of software/data engineering experience, including hands-on experience with knowledge graphs or graph technologies âą Strong understanding of knowledge graph and graph data modeling concepts âą 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 âą Working knowledge of, or ability to work across, both LPG and RDF graph models âą Strong programming skills in Python, Java, or similar languages âą Experience building production data ingestion, transformation, or integration pipelines âą Experience with graph databases and/or RDF triple stores âą Understanding of entity identity, graph relationships, mappings, provenance, and data quality âą Ability to translate conceptual models and mapping specifications into production implementations âą Ability to work effectively with evolving requirements and cross-functional technical/domain teams âą Hands-on experience with both LPG and RDF/semantic knowledge graphs âą Healthcare or life sciences data experience âą Familiarity with HL7 FHIR, including Coding, CodeableConcept, Reference, and Identifier âą Experience with ontology-driven data integration or semantic mapping âą Experience with SHACL or other graph/semantic validation approaches âą Experience with terminology/concept normalization and canonical concept mapping âą Familiarity with clinical terminologies such as SNOMED CT, LOINC, RxNorm, ICD, or UMLS âą Cloud and CI/CD experience
âą Upskilling and professional development opportunities âą Exposure to AI, ML, data, and cloud technologies âą Opportunity to work with Fortune 1000 companies âą Research-focused work environment âą Opportunity to work alongside passionate, talented colleagues âą Opportunity to work with disruptive innovators âą Remote work arrangement âą Travel opportunities to client sites across the US
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