Healthcare Data Engineer & Data Architect
Building production-grade healthcare data platforms with OMOP CDM standardization, multi-cloud architecture (Azure + Snowflake), and comprehensive data governance through Microsoft Purview and Fabric
Production-ready data engineering solutions across diverse industries
FHIR-OMOP interoperability showcase with synthetic patient data and CMS hospital insights
Brazilian e-commerce marketplace analysis with order lifecycle and customer segmentation
Sample Superstore sales performance and customer profitability analysis
Real-time SP100 monitoring with 15-minute incremental updates
Multi-cloud platforms and enterprise-grade tools for production data engineering
Unified data platform leveraging the Microsoft ecosystem
Microsoft Fabric serves as the unified analytics platform, integrating Data Factory for ETL orchestration, Synapse for data warehousing, and Power BI for visualization—all within a single SaaS environment. Microsoft Purview provides the governance layer, automatically cataloging data assets, tracking lineage across the entire pipeline, and enforcing compliance policies. Together with Azure's enterprise-grade security and Snowflake's computational efficiency, this stack enables scalable, governed, and interoperable healthcare data platforms that meet both regulatory requirements and analytical demands.
Production-grade patterns for scalable healthcare data platforms
Bronze (raw) → Silver (OMOP standardized) → Gold (analytics-ready) with clear separation of concerns and data lineage tracking
Azure Data Factory + Snowflake SQL for scalable ETL/ELT, SSIS for legacy integration, cross-cloud data movement patterns
OMOP CDM v5.4 as canonical data model enabling federated analytics, standardized vocabularies, and research network participation
DataQualityDashboard with 3,500+ validation checks, Kahn Framework dimensions, automated monitoring achieving 95%+ quality scores
Bidirectional transformation pipelines, USCDI compliance, Bulk FHIR exports, legacy HL7v2 to FHIR R4 migration workflows
Change data capture (CDC), incremental loading, watermark-based processing, error handling, idempotency, comprehensive monitoring
Core principles that guide architecture decisions
OMOP CDM standardization enables trust, interoperability, and federated analytics—don't scale fragmentation
Data quality frameworks, lineage tracking, and metadata catalogs are product features that enable AI and ML
Machine learning predictions require clinical SME review, bias detection, and continuous monitoring in healthcare
Azure + Snowflake integration patterns provide flexibility, cost optimization, and best-of-breed capabilities
FHIR-OMOP bidirectional mapping is ongoing architecture work, not one-time integration—plan for evolution