Data Architect

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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
• Data platform and metadata-driven architecture patterns

PREFERRED QUALIFICATIONS

• HIE connectivity (Carequality, CommonWell, Direct Secure Messaging)
• 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