Rosario Di Somma
About Rosario Di Somma
Rosario Di Somma is a Lead Architect at DreamHost, where he has worked since 2018. He has a background in software engineering and has developed systems to enhance customer experience and product planning.
Work at DreamHost
Rosario Di Somma has been a key member of the DreamHost team since 2014. Initially serving as a Python Software Engineer and Software Reliability Engineer from 2014 to 2017, he contributed to various projects aimed at enhancing software reliability. In 2018, he transitioned to the role of Lead Platform Architect, where he has focused on improving platform architecture and performance. Since 2019, he has also held the title of Lead Architect, overseeing architectural decisions and guiding development teams. His work includes developing systems that enhance customer experience and implementing metrics systems for legacy systems.
Education and Expertise
Rosario Di Somma studied Computer Science at Università di Pisa, where he earned a Bachelor of Science (BS) degree in 2008. His educational background laid the foundation for his career in software engineering and architecture. His expertise includes Python programming, software reliability, and system architecture. He has applied his knowledge in various roles, particularly in developing distributed systems and implementing metrics systems within cloud environments.
Background
Prior to joining DreamHost, Rosario Di Somma worked as a Software Engineer at Reflab s.r.l. from 2006 to 2008 in Pisa, Tuscany, Italy. This early experience contributed to his technical skills and understanding of software development processes. His career has since evolved, leading him to significant roles in a major hosting and cloud services provider in the United States.
Achievements
During his tenure at DreamHost, Rosario Di Somma has developed a distributed system to gather metrics on customer experience and interaction with DreamHost's products. He has also implemented a metrics system on top of Kubernetes for legacy systems, enhancing operational efficiency. Additionally, he presented a Machine Learning concept aimed at assisting product owners in planning new products based on behavior pattern identification, showcasing his innovative approach to product development.