• Built an automated data validation system using Python and QlikSense that verifies 800K+ daily values between Amazon and internal databases, expanding data coverage and highlighting discrepancies previously missed by manual reviews

• Developed a scalable Amazon product scraper in Python by containerizing Playwright in Docker with parallel execution, reducing validation workflows from days to under 30 minutes and cutting data mismatches by 50%

• Designed a Purchase Order matching module combining internal MSSQL and customer Postgres data, enabling accurate audits, inventory control, and sales order management across company and factory POs

• Created an interactive forecasting dashboard in Next.js and React consolidating 10+ API outputs on purchasing trends, inventory projections, and purchase orders, enabling CFO and e-commerce team to optimize planning and ordering

• Implemented a CLIP-based AI image comparison pipeline that detects mismatches across 1800+ Amazon and internal product images, replacing broad manual reviews with targeted analysis and improving product page quality

• Architected a Docker Compose and NGINX deployment framework isolating dev and production services in a shared network, streamlining deployments and enhancing environment security

• Automated ticketing workflows in legacy ERP system, significantly reducing manual entry by 90%, improving communication with production team, and boosting packaging throughput

• Led a 5-member Agile team in developing NEO, a developer tool visualizing Technical SEO metrics for Next.js projects, enhancing app SEO with data-driven optimizations

• Achieved 100% overhead cost reduction and significantly boosted performance by reengineering NEO as a VSCode extension, eliminating AWS server infrastructure and Docker

• Upgraded developer experience with interactive React Vite Webview panel, VSCode API, and custom React chart components for a clean and intuitive interface

• Optimized SEO analysis by using Puppeteer to access local instances and navigate pages, leveraging Performance API for extracting 6 core performance metrics

• Enhanced data accuracy and usability by designing an algorithm that transforms raw performance data into scores

• Enforced static type error handling with TypeScript ensuring scalability, code readability, and minimized runtime errors

• Improved performance and render speed by ~64% using Recharts and a custom useMetrics state hook, eliminating previously needed useEffect and Canvas API

• Developed mobility aid for the visually impaired, delivering depth perception and obstacle avoidance cues through intuitive audio feedback, enhancing users navigation accuracy and safety in environments with overhead obstacles

• Engineered intuitive spatial audio feedback protocol with 90.4% user accuracy using MATLAB, which converts single-point LiDAR camera distance data transmitted via Arduino into comprehensible frequencies relating to distance

• Designed testing environment to assess feedback intuition and collect user data while navigating obstacle course blindfolded

• Analyzed system integration data using Python, Pandas, and Seaborn to show ~2.5x improvement in user’s obstacle avoidance

• Conducted in-depth literature review regarding existing visual mobility aides, visually impaired navigation, electronic white canes, and object detection algorithms