James – React, Typescript, Java

An enterprise payroll and financial systems modernization initiative for a major international retailer, focused on transitioning legacy mainframe infrastructure to cloud-native microservices, improving API compatibility, and ensuring reliable processing of over 2 million weekly employee payroll records.

• Designed a data-oriented RESTful API protocol using TypeScript, decoupling Java backend microservices from React frontend development while maintaining compatibility across millions of clients;
• Optimized distributed Azure SQL databases, eliminating database locking during peak financial processing for over 2 million weekly payroll records;
• Established containerized CI/CD pipelines and automated validation gates to safely test third-party hotfixes for mission-critical enterprise systems;
• Led engineering teams through legacy-to-cloud modernization, transitioning mainframe-based systems toward cloud-native microservices;
• Implemented system observability and monitoring using Dynatrace and custom telemetry metrics;
• Researched machine learning and AI agents for automated payroll verification.

An open-source Java framework for managing, querying, and processing RDF graph data using Semantic Web standards, including RDF and SPARQL, supporting applications that work with structured and interconnected data.

• Led the technical roadmap, backlog prioritization, intellectual property compliance, and global open-source releases;
• Initiated performance benchmarking and cost-based query execution optimizations, contributing to a 40% increase in active adoption;
• Modernized continuous integration and testing infrastructure by migrating automated build and test workflows to Jenkins and Travis CI.

A medical data integration platform developed for a U.S. Department of Defense project, focused on consolidating data from multiple sources into unified domain models while maintaining data provenance and lineage. The solution also supported semantic process modeling and interactive workflow management for medical data pipelines.

• Developed high-throughput Python ETL pipelines to ingest and normalize data from MongoDB and PostgreSQL into unified domain models, preserving data provenance and lineage;
• Designed semantic process modeling systems and interactive workflow editors for medical data pipelines.

An enterprise content integration platform designed to connect and manage structured data from multiple sources using Linked Data and knowledge graph technologies. The platform supported dynamic graph querying, secure data access, and integration with external enterprise systems.

• Implemented Linked Data Platform services, server-side HTTP caching, and optimistic concurrency control to support scalable knowledge graph querying;
• Architected enterprise authentication and authorization layers using OAuth/OpenID, along with RESTful API gateway integrations;
• Developed administration web applications and configured automated cross-browser testing with Selenium and custom Java execution security policies.