Enterprise Healthcare Data Orchestration & Session Pipeline
A high-throughput integration engine designed to unify multi-platform healthcare data streams across OneWell ecosystem applications (including C2S via iCodeBetter, Envolve Health, and state EIM-Enterprise Incident Management).
Context
Healthcare / Government
Scope
Enterprise Analytics & Exception-Routing Platform
Ownership
End-to-End Ownership
Problem
OneWell utilized multiple critical platforms to manage patient care and incident tracking, including Envolve Health, state portals, and a core low-code platform (C2S on iCodeBetter). Because these external environments lacked public API endpoints, data synchronization required extensive manual entry, creating operational bottlenecks and data latency.
Approach
To bridge these systems without vendor-level modifications, I analyzed the underlying data communication protocols and payload requirements of each platform. Architected a custom synchronization engine that managed secure session-state preservation and programmatic data handling. This allowed our application layer to securely read and transmit assessment data directly between the low-code environment and target portals without requiring manual human intervention.
Results
Reduced incident response orchestration time by 40% and automated 100% of compliance reporting compliance logs. Delivered multi-level analytics system, enabling real-time decision-making, and supporting high-volume operations. Improved data quality and reliability through automated validation pipelines.
Role & Responsibilities
Lead Engineer - Architected system design, developed event-driven analytics dashboards, optimized SQL queries, built ETL pipelines, and delivered the end-to-end solution
Technologies Used
Interview Talking Points5 Points2 Challenges3 Decisions
Interview Talking Points
Led end-to-end architecture of a multi-dashboard analytics system serving enterprise, regional, and program-level stakeholders with real-time KPIs
Optimized complex SQL queries using CTEs and window functions, reducing query execution time and enabling real-time analytics on large datasets
Designed and implemented automated ETL pipelines with Excel-based validation, eliminating manual data processing and improving data quality
Built scalable React dashboards with role-based access control, enabling stakeholders at different organizational levels to access relevant insights
Delivered production system supporting high-volume operations with measurable impact on reporting efficiency and decision-making speed
Challenges
Balancing performance requirements with complex multi-level aggregations across large healthcare datasets
Ensuring data quality and reliability in automated pipelines while maintaining flexibility for changing business requirements
Key Decisions
Chose PostgreSQL with strategic indexing and CTEs over NoSQL for complex relational queries and analytics requirements
Implemented hierarchical dashboard architecture to serve different stakeholder needs while maintaining a single source of truth
Built Excel-based ETL validation layer to leverage existing business user expertise while automating manual processes