Prashanth Vaida
About Prashanth Vaida
Prashanth Vaida is a Data Engineer and Data Visualization Expert currently working at Fannie Mae in the Washington D.C. Metro Area. He has extensive experience in data engineering, business intelligence, and data architecture across various industries.
Title
Prashanth Vaida serves as a Data Engineer and Data Visualization Expert.
Current Role at Fannie Mae
Prashanth Vaida is currently employed by Fannie Mae as a Data Engineer and Data Visualization Expert in the Washington D.C. Metro Area. In this role, he is involved in developing data infrastructure using AWS 'big data' technologies, enhancing data delivery, and creating analytics tools to improve customer acquisition and operational efficiency.
Experience at ExxonMobil
At ExxonMobil, Prashanth Vaida held multiple roles, including Data Engineering Lead, Data Architect, and Data Science Specialist from 2018 to 2019. Previously, he worked there from 2010 to 2012 as a Senior Data Migration Consultant. His responsibilities included re-designing infrastructure for greater scalability and automating manual processes.
Roles at Lockheed Martin
Prashanth Vaida worked at Lockheed Martin in various capacities from 2012 to 2018. He served as a Team Lead, Enterprise Data Warehouse Architect, BI Solutions Architect, and Business Objects Architect in Crystal City, VA. His tenure spanned six years, during which he focused on data architecture and business intelligence.
Educational Background
Prashanth Vaida studied at the University of South Alabama, earning a Master's degree in Engineering from 2005 to 2007. He also holds a Bachelor's degree in Technology from Jawaharlal Nehru Technological University, completed in 2003. Earlier, he attended Nayana Junior College, where he studied Mathematics, Physics, and Chemistry.
Expertise in Data and Visualization Tools
Prashanth Vaida is skilled in using various data-discovery tools for enterprise solutions, including Tableau, SAP Business Objects, and Alteryx. His expertise also extends to creating analytics tools and re-designing infrastructure for improved scalability and efficiency in data delivery.