• Enhanced existing ETL pipelines, in order to enable marketing for 220,000+ users, across 45 user actions, allowing accurate campaigns which led to significant increases in user monthly engagement• Integrated backend into outside API, using Django and Python, to allow users to purchase memberships to outside services using Stripe, allowing for new revenue streams on existing and future subscriptions.• Automated report generation using SQL to ensure accurate information for customer billing every quarter.As of January 2023, Burnalong had been acquired by Tivity Health

• Engineered scalable ETL microservices utilizing SQL, Flask, and Python to process and analyze data from 190,000 users, enhancing the analytics dashboard and consumer engagement platform.• Designed and deployed RESTful APIs and database schemas with Django and JavaScript to efficiently track and store user interactions.• Automated critical business workflows using Django, HTML, and AWS S3, streamlining site management and supporting staff operations.• Collaborated with cross-functional teams to convert business requirements into precise technical specifications, optimizing front-end integration.• Led backend development for key site features including video playlists and a microservice for managing medical referrals, leveraging Django and Python for robust and scalable solutions.

• Part of software team responsible for maintaining and enhancing backend client which acts as a layer betweenhardware and front-end client for thousands of metrology machines• Responsibilities include writing unit tests, using CppUnit to follow test-driven development principles andenhancing existing features by augmenting or exposing data from the API, all in a team setting• Tech stack includes Windows, managed/unmanaged C++, C#, and Visual Studio 2019

• Helped in improving feature matching in computer vision algorithm using OpenCV and C++, leading to full recognition ina previously unaccounted for edge case containing 1,694 images• Created web app employing Flask, Javascript, SQLite, and AWS, to assist with generating new data sets foran ML model, cutting down data labeling time from 4 hours to 2 hours per 1,000 images• Used a combination of AWS EC2, Boto3, and S3 to automate the storage and retrieval of training data toassist with the implementation of CI/CD of codebase containing computer vision code• Helped document existing processes and company practices such as deploying to production and initialenvironment set up for code base

• Worked on the parallel computing framework skool in C++ for Dr. Jaroslaw Zola• Read, summarized, and presented research papers related to Traveling Salesman Problem• Wrote algorithms in pseudocode, to showcase approach for solving TSP with skool

• Integrated new libraries and software into the companies’ existing tech stack by writing documentation, creating example projects, and training coworkers• Setup simulations using IsaacSim, IsaacSDK, and PCL to collect point cloud data from simulated lidar sensors• Helped develop and debug drivers and visualizers in C++ for lidar sensors such as the OS1 and VLP16