Job description

OVERVIEW OnTrac is seeking a highly skilled and experienced Data Architect to join a contractor program supporting a healthcare / pharmacy benefit management (PBM) customer. The role owns architecture and technical delivery for a data platform expansion covering rebate reconciliation, referral capture, FHIR / HL7 interoperability, EDI transaction processing, and clinical data exchange. It is ideal for a hands-on architect who designs scalable, metadata-driven ingestion patterns that support current and future healthcare and pharmacy data feeds. The successful candidate will work closely with client stakeholders and distributed delivery teams to ensure project success from initial definition through final delivery. ENGAGEMENT • Location: United States, fully remote REQUIRED QUALIFICATIONS • Azure Data Factory, Databricks, and Spark-based data engineering • HL7 v2 (ADT, SIU) and FHIR, including resource modeling and terminology mapping • X12 EDI parsing and transformation experience • Strong API design background • Data platform and metadata-driven architecture patterns PREFERRED QUALIFICATIONS • Healthcare payer / PBM or pharmacy rebate domain experience • 340B program knowledge • HIE connectivity (Carequality, CommonWell, Direct Secure Messaging) • HCPCS / J-Code familiarity • Patient matching and identity resolution experience SCOPE AND DELIVERY EXPECTATIONS The following covers the scope of work we anticipate the contractor supporting throughout the project. This scope is in line with our expectations of the project but is subject to change. • Architect ingestion pipelines for X12 EDI (835, 837, 832, 810) and MTF files using Azure Data Factory and Databricks • Design FHIR-based ingestion patterns (API and bundle) with incremental sync, throttling, and retry handling • Define canonical data models for referral, clinical document, and provider-administered drug data • Extend the existing metadata-driven ingestion framework to support new payload types without platform redesign • Architect HL7 ADT / SIU linkage supporting 340B eligibility determination • Design clinical document storage, indexing, and retrieval architecture (CCD / C-CDA) • Establish trading partner onboarding and configuration patterns • Guide reporting and analytics data model design (tables and views for BI consumption) • Partner with client stakeholders and vendor teams on integration points GROWTH EXPECTATIONS • Comfort using AI-assisted development tools (for example, GitHub Copilot or Claude Code) as force multipliers. Not required on day one; expected to develop. COMPETENCIES • Adaptability across ambiguous, evolving scope • Clear cross-functional communication with the client and distributed delivery teams • Ownership and prioritization across multiple concurrent workstreams

Requirements

Must-haves

Location: United States, fully remote
Azure Data Factory, Databricks, and Spark-based data engineering
HL7 v2 (ADT, SIU) and FHIR, including resource modeling and terminology mapping
X12 EDI parsing and transformation experience
Strong API design background
Data platform and metadata-driven architecture patterns
The following covers the scope of work we anticipate the contractor supporting throughout the project. This scope is in line with our expectations of the project but is subject to change.
Architect ingestion pipelines for X12 EDI (835, 837, 832, 810) and MTF files using Azure Data Factory and Databricks
Design FHIR-based ingestion patterns (API and bundle) with incremental sync, throttling, and retry handling
Define canonical data models for referral, clinical document, and provider-administered drug data
Extend the existing metadata-driven ingestion framework to support new payload types without platform redesign
Architect HL7 ADT / SIU linkage supporting 340B eligibility determination
Design clinical document storage, indexing, and retrieval architecture (CCD / C-CDA)
Establish trading partner onboarding and configuration patterns
Guide reporting and analytics data model design (tables and views for BI consumption)
Partner with client stakeholders and vendor teams on integration points
Comfort using AI-assisted development tools (for example, GitHub Copilot or Claude Code) as force multipliers. Not required on day one; expected to develop.
Adaptability across ambiguous, evolving scope
Clear cross-functional communication with the client and distributed delivery teams

Nice-to-haves

Healthcare payer / PBM or pharmacy rebate domain experience
340B program knowledge
HIE connectivity (Carequality, CommonWell, Direct Secure Messaging)
HCPCS / J-Code familiarity
Patient matching and identity resolution experience

Application prep

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Keywords to mirror

azureapiai

Platform: breezy
Effort: medium