Passionate about leveraging data to drive actionable insights and fuel innovation, I bring extensive experience in data engineering and data science to the table. With a Master's degree in Computer Science specializing in Machine Learning from Georgia Tech, and a background in Information Technology from Vellore Institute of Technology, I have honed my skills in Python, SQL, ETL processes, cloud platforms like AWS and Azure, and advanced machine learning techniques. In my current role as a Data Engineer Assistant at Georgia Tech, I work alongside astrophysics experts, applying advanced statistical techniques to model correlated noise in transit light curves for exoplanet detection. I have architected end-to-end data pipelines handling vast amounts of telescope data, implemented serverless data pipelines on AWS for real-time data streaming, and orchestrated successful data migrations for stock market data using AWS DynamoDB, S3, and Snowflake. During my tenure at PricewaterhouseCoopers (PWC), I led data engineering and data science initiatives for Fortune 500 clients. Notable achievements include optimizing big data transformations, deploying over 100 data pipelines using AWS technologies, and integrating efficient data ingestion from Google Pubsub. My expertise extends to machine learning model development, A/B testing, and predictive analytics, where I have consistently delivered tangible business results. I thrive in dynamic, collaborative environments where I can apply my technical skills to solve complex problems and drive strategic initiatives. Let's connect to discuss how I can contribute to your organization's data-driven success.