Alexander Urmaza
About Alexander Urmaza
Alexander Urmaza is a Software Engineer specializing in Collateral Management at Fannie Mae in the Washington DC-Baltimore Area. He holds a BS in Computer Science & Applied Math with a concentration in Data Analytics from the University at Albany, where he graduated Summa Cum Laude.
Company
Alexander Urmaza is currently employed at Fannie Mae, serving as a Software Engineer in the Collateral Management division. Fannie Mae is known for its role in the mortgage industry and aims to facilitate equitable and sustainable access to homeownership.
Title
Alexander Urmaza holds the title of Software Engineer with a focus on Collateral Management at Fannie Mae. His role involves developing and maintaining software solutions that streamline and manage the organization's collateral obligations.
Education and Expertise
Alexander Urmaza graduated Summa Cum Laude from the University at Albany, where he earned a Bachelor of Science degree in Computer Science and Applied Mathematics, with a concentration in Data Analytics. He also studied Biomedical Engineering at the University of North Carolina at Chapel Hill for one year. His academic background equips him with a robust understanding of both data analytics and engineering principles.
Background
Alexander Urmaza began his career with internships and various roles that laid a strong foundation for his current position. He served as a Software Engineer Intern at Fannie Mae in 2022, gaining valuable experience over two months. He also held positions at the Institute of Electrical and Electronics Engineers (IEEE) University at Albany Student Branch as Treasurer and a member of the Web Development Team, and previously worked as a Front of House Supervisor at Baba's Pizzeria.
Achievements
One of Alexander Urmaza's notable achievements is developing software utilizing the Java Spring Framework to generate consolidated comparison reports from various collateral positions. This software helps the Collateral Integration Manager at Fannie Mae quickly identify discrepancies in thousands of collateral positions processed daily.