•Led the development of an ETL pipeline to transfer user data from the CFL website to S3 and Snowflake, optimizing the process with AWS Lambda, resulting in a 4x increase in data transfer speed.
•Developed a Python script with Pandas and Snowflake connector to synchronize data across all Braze and Rudderstack APIs, increasing data availability by 2x and reducing associated AWS costs by 15%.
•Maintained data accuracy with Postman API testing, debugging, automated test scripts, and code reviews, improving testing efficiency by 100% compared to manual processes.
•Used GitHub for version control and CI/CD integration. Led Agile projects with Scrum sprints. Presented findings that changed project trajectory to improved pipeline efficiency.

•Developed a machine learning model using XGBoost and ridge regression to forecast real-time market data.
•Evaluated model performance using R² values, optimizing for accuracy through parameter tuning.
•Demonstrated real-world applicability by accurately predicting market trends, achieving a top 10% ranking in the competition among thousands of participants.

•Analyzed Bitcoin cryptocurrency prices correlation with the Fear and Greed Index using Pandas and mathematical models, identifying it as a lagging indicator through data visualization.
•Applied machine learning to create trading strategies, implementing and evaluating them using TradingView Pine Script.