•Enabled zero-downtime adoption and data consistency by building automated migration and bi-directional sync pipelines for 5 million records across 16 legacy systems using Node.js, Sequelize, cron jobs, and REST APIs.
•Accelerated platform adoption while minimizing refactor risk by developing a Python/FastAPI abstraction layer that allowed legacy services to perform CRUD operations against the new platform without direct dependency changes.
•Reduced migration coordination overhead by 90% by designing a network-level abstraction and traffic-routing layer that preserved backward compatibility for 60+ internal services during phased legacy deprecation.
•Streamlined scientist workflows and eliminated data ambiguity by leading cross-team domain modeling to unify fragmented schemas and workflows across 16 legacy applications into a single internal data platform.
•Increased system reliability and incident detection by 40% by building observability dashboards using SQL, Dynatrace, and CloudWatch logs, identifying bottlenecks across APIs, databases, and infrastructure.