Developed a full-stack Data Monitor module that detects private market data anomalies and inconsistencies in the data syncing process from BlackRock's subsidiary, eFront, into Aladdin, and reducing troubleshooting time from 3-4 days to just a couple of hours, using Java, Spring, React, Apache Kafka, Tailwind CSS, Mockito, Jest, JUnit, Figma.
Implemented a multithreaded application software utilizing Python and PyQT5, following OOP paradigm to parse and upload data from proprietary devices’ generated test reports, successfully handling up to 250 reports daily.
Developed a full-stack real-time data monitoring application employing Express, NodeJS, TypeScript, NextJS, Zod with RTK query and Redux Toolkit following MVC paradigm and REST API.
Utilized Azure Blob Storage and Azure Cosmos DB with MongoDB API to establish a robust NoSQL database, supporting over 1,000 queries per day and ensuring real-time data management for the website’s querying needs.
Migrated the preexisting NextJS and Express project to React, ASP.NET, improving performance by 1.5x, reducing the codebase size and maintenance effort by 25%, thereby enhancing overall efficiency and security.
Implemented and conducted rigorous regression testing and unit testing with pytest, achieving 95% test coverage, which reinforced the reliability and stability of the software.
Managed the deployment of software to 100 internal test rigs and set up an Azure CI/CD pipeline.
The two-part project detected issues 45% earlier, allowing people to make faster decisions, resulting in a 60% reduction in resolution time and preventing an estimated $20,000 in costs from manufacturing defects annually.
Designed a full-stack web app that processes raw data, dynamically runs the lottery
with multiple variables for over 2000 entries and generated a report for the safety
department’s hazard recognition payout program. The web served approximately 100
internal users.
Worked with GIS or geographic information system team to create accurate geospatial
data of operational pipelines throughout North America for easy maintenance and
prevention of spills/environmental violations.
Reduced the network latency by 20% by
building a local cache of map data and hiding certain layers and features
automatically.
