Improved the operational costs of a newly acquired system restructuring its microservices architecture, reprovisioning over-provisioned machines, and migrating the system from a mix of Google Cloud and VMWare stack to an AWS Stack, the total realizable operational savings of this Lift & Shift were in the order of $2.5 million USD. The migration involved refactoring services and serverless functions in various languages, targeting mainly Typescript (Node) and Python (Flask),. And their deployment in EC2 instances, ECS Fargate containerized instances, AWS Lambdas orchestrated with Step Functions, creation of Kinesis Delivery Streams for ingestion, Athena Tables, and Queries for data processing in the order of 10 TB / month. Worked in a Fullstack application in the field of data analytics and visualization. React FE, Flask and Typescript BE, AWS. Improved the performance of multiple workflows by introducing client-side and server-side caching.

Improved the performance and reliability of a retail web application frontend, reducing the number of bugs and issues by 50% by refactoring modules, identifying processes implemented in quadratic or exponential time complexity, then rewriting them in better time complexity. Stack: BE microservices in Python, Node and PHP; FE in React and some pure JavaScript. ● Improved a system’s reliability and efficacy reducing the monthly faulty transactions by around 20,000. I tracked and analyzed the faulty transactions at different layers in the application stack and was able to identify the root cause as a concurrency issue. Replicated the problem and proposed a solution. Work consisted of analysis in a SQL Server Layer. .Net and Node microservices, and tracing in a windows server environment. ● Worked on a client’s Health Analytics platform. Both in the Backend: Python with Flask and SQLAlchemy as ORM targeting a mix of Postgres and ElasticSearch Data Layer. The frontend in React. Unit testing was done with Pytest on the BE and Cypress/Jest on the FE.