# Nitesh S.

> Software Engineer | Python, TypeScript, Data Pipelines | Bioengineering Background

Location: New York City Metropolitan Area, United States
Profile: https://flows.cv/niteshs

Hi, I'm Nitesh (Nith-aysh) Sunku (Sun-koo).

I am a full-stack developer with the goal of creating innovative and inclusive products that advance our general welfare and improve user experiences.

I am a co-creator and active contributor to NEO, an open-source dev tool that helps Next.js devs make data-driven decisions on improving their SEO measures during their app's developmental stage.

I designed and programmed an audio feedback protocol with 90% accuracy that translates LiDAR data into intuitive audio feedback for visually impaired individuals.

I also work on various side projects, check it out on Github: github.com/nsunku99

When not working, I'm usually cooking new recipes, baking a lot of sourdough, or playing tennis.

Feel free to reach out if you'd like to chat!

## Work Experience
### Software Engineer @ Britannica Home Fashions, Inc.
Jan 2024 – Present | New York, New York, United States
- 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

### Software Engineer @ Next Engine Optimization (Open Source)
Jan 2023 – Jan 2023
- 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

### Software Systems Engineer @ Integrated Systems Neuroengineering Lab
Jan 2021 – Jan 2022 | University of California, San Diego
- 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


## Education
### Bachelor of Science - BS in Bioengineering: BioSystems
UC San Diego


## Contact & Social
- LinkedIn: https://linkedin.com/in/niteshsunku
- GitHub: https://github.com/nsunku99

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Source: https://flows.cv/niteshs
JSON Resume: https://flows.cv/niteshs/resume.json
Last updated: 2026-04-01