Healthcare Data Architecture :

- Design and evolve enterprise healthcare data architectures supporting clinical, revenue cycle, operational, and financial domains.

- Design scalable logical and physical healthcare data models that support interoperability, analytics, and operational workloads.

- Define enterprise data modeling standards, metadata management practices, and architectural best practices.

- Partner with engineering and product teams to establish reusable architecture patterns for healthcare data platforms.

HL7 FHIR Architecture & Implementation :

- Lead enterprise implementation of HL7 FHIR (R4/R4B/R5) leveraging Implementation Guides (IGs), Profiles, Extensions, StructureDefinitions, and Terminology Services.

- Design FHIR-native integration strategies and implement best practices for FHIR profiling, validation, conformance, and interoperability.

- Design healthcare data harmonization strategies that standardize and normalize data from diverse healthcare sources into consistent, analytics-ready information models.

- Develop interoperability and transformation strategies for healthcare data exchanged through HL7 v2, FHIR, EDI, APIs, and other industry-standard formats while maintaining semantic consistency and data quality.

Data Platform & Pipeline Engineering :

- Design, develop, and maintain scalable ETL/ELT pipelines for clinical, revenue cycle, financial, operational, and workflow-related healthcare data.

- Develop ingestion and transformation pipelines for HL7 v2, FHIR APIs, EDI transactions, and file-based healthcare data sources.

- Build and optimize enterprise data models within modern cloud data warehouses to support analytics, reporting, and AI/ML workloads.

- Design and implement streaming and event-driven data pipelines for near real-time healthcare applications.

- Develop robust orchestration frameworks that deliver reliable, monitored, and observable data workflows.

Cloud Infrastructure & Architecture :

- Architect secure, scalable, and resilient healthcare data platforms across leading cloud platforms (AWS, Azure, or Google Cloud).

- Design cloud-native data architectures using modern data storage, processing, messaging, orchestration, and analytics services.

- Implement Infrastructure as Code (Terraform or equivalent) and cloud architecture best practices.

- Design scalable data lake and lakehouse architectures to support structured and semi-structured healthcare data.

- Optimize cloud infrastructure for performance, scalability, security, reliability, and cost efficiency.

Healthcare Data & Compliance :

- Ensure healthcare data platforms comply with HIPAA, HITECH, and applicable healthcare privacy and security regulations.

- Partner with clinical, security, and compliance teams to implement data governance, metadata management, lineage, cataloging, and access controls.

- Apply healthcare terminology standards including SNOMED CT, LOINC, ICD-10, CPT, and RxNorm to support semantic interoperability and standardized clinical data.

Data Quality, Observability & Leadership :

- Define and implement enterprise data quality, validation, observability, and monitoring frameworks to ensure completeness, consistency, conformance, and integrity of healthcare data.

- Build monitoring capabilities to proactively detect data anomalies, schema drift, pipeline failures, and operational issues.

- Mentor architects and data engineering teams through architecture reviews, technical guidance, and engineering best practices.

- Partner with Data Science, AI/ML, Product, and Engineering teams to deliver trusted, high-quality datasets supporting advanced analytics and intelligent applications.

Preferred candidate profile :

Professional Experience :

- 12+ years of experience in enterprise data engineering and architecture, with significant experience in cloud-native healthcare platforms.

- Proven experience designing scalable healthcare data architectures and enterprise data platforms.

- Strong experience working with clinical, revenue cycle, financial, and operational healthcare data.

FHIR Implementation Expertise :

- Extensive hands-on experience implementing HL7 FHIR standards in enterprise production environments.

- Strong knowledge of FHIR Implementation Guides, Profiles, Extensions, StructureDefinitions, Terminology Services, and interoperability best practices.

- Demonstrated experience integrating and harmonizing healthcare data across HL7 v2, FHIR, EDI, APIs, and other healthcare data formats.

- Experience working with FHIR-enabled healthcare platforms such as Google Cloud Healthcare API, Azure Health Data Services, AWS HealthLake, HAPI FHIR, Firely, Smile CDR, or equivalent technologies.

Technical Proficiency :

- Strong programming experience with Python and SQL.

- Experience with modern cloud data warehouses such as BigQuery, Snowflake, Redshift, Synapse, or equivalent platforms.

- Hands-on experience with ETL/ELT frameworks, dbt, and workflow orchestration tools such as Airflow, Prefect, Dagster, or equivalent.

- Experience designing streaming data pipelines and event-driven architectures.

- Experience with Infrastructure as Code using Terraform or equivalent technologies.

Healthcare Domain Expertise :

- Strong understanding of healthcare interoperability standards including HL7 v2, HL7 FHIR, C-CDA, X12, SMART on FHIR, ICD-10, SNOMED CT, LOINC, CPT, and related standards.

- Experience integrating with enterprise EHR/EPM platforms such as Epic, Oracle Health (Cerner), NextGen, Athenahealth, ModMed, MEDITECH, Veradigm, eClinicalWorks, or similar healthcare systems.