As a Business Intelligence Engineer at Amazon, I leverage SQL, Python, and Tableau to analyze large-scale data sets, create dashboards and reports, and measure key performance indicators for various business units. Before joining Amazon, I was a quantitative engineer at Goldman Sachs, where I developed and deployed machine learning models for bond pricing and trade execution. I also collaborated with traders, analysts, and risk managers to provide data-driven insights and recommendations. Additionally, I have experience as a consultant at Deloitte, where I performed risk advisory, internal audit, and business strategy projects for clients across different industries. I hold a master of science degree in analytics from the University of Southern California, where I was the head of operations and finance for the Business Analytics Student Association. I also worked as a graduate research and teaching assistant, conducting exploratory data analysis and linear optimization on various topics. I have a bachelor of science degree in finance from Penn State University, where I was a Smeal Honor Code recipient and a Dean's List scholar. I am passionate about applying data analytics and machine learning to solve real-world problems and create value for businesses and customers. I have strong analytical, communication, and leadership skills, and I enjoy working with diverse and collaborative teams. My goal is to continue learning and growing as a business intelligence engineer and a data science professional.