Jay A Prombo

Designed, developed, and maintained backend platforms supporting enterprise healthcare applications across payer, provider, pharmacy, and care delivery workflows. Built production Python services and APIs processing sensitive operational information with emphasis on reliability, maintainability, and secure integration. Developed database-backed microservices using Python (FastAPI/Flask), Java, SQL, PostgreSQL, Redis, and Kafka. Implemented REST API authentication, session management, and authorization patterns for secure communication between internal applications and external services. Refactored legacy backend components to improve error handling, simplify maintenance, strengthen reliability, and reduce production risks. Added controlled deployment checkpoints, runtime safeguards, rollback mechanisms, and security review gates. Conducted security testing and remediation across APIs, authentication mechanisms, data pipelines, and integrations, while documenting architecture decisions,

Designed and deployed machine learning and data-driven solutions for clinical analytics, healthcare research workflows, and operational decision-making. Built NLP pipelines for clinical text processing such as entity extraction, classification, and structured insight generation. Developed Python-based ML services and data pipelines for preparing and processing large-scale healthcare datasets. Created ETL/data processing workflows using Python, PySpark, SQL, and Apache Airflow. Integrated healthcare data from claims, clinical, laboratory, and EHR-related sources, including interoperability work with HL7 and FHIR standards. Supported productionization with model versioning, reproducibility, monitoring, and repeatable deployment processes, and contributed to responsible AI practices including validation, explainability, bias review, and performance monitoring.

Supported data engineering initiatives across pharmacy, claims, prescription benefit, healthcare analytics, and operational reporting platforms. Built Python and SQL ETL workflows to ingest, clean, transform, and validate healthcare and business data from multiple sources. Developed data pipelines for analytics, reporting, operational dashboards, and downstream workflows. Automated recurring data validation, reconciliation, and transformation to improve accuracy and delivery speed. Improved data quality through validation checks, error handling, logging, and repeatable processing standards while following governance, access control, and data protection practices. Documented pipeline mappings and operational support procedures and assisted with performance tuning of SQL queries, batch jobs, and data workflows.