WekaIO Announces Expansion with new Appointments
WekaIO recently announced it has opened a new office in Detroit, Michigan. Besides the new office the company also announced the appointment of Mike Guentner as Regional Sales Manager and Tony Raleigh as Senior Systems Engineer. WekaIO that the new office as well as the hirings in the Midwest region are vital to its growth in the automotive industry.
Richard Dyke, VP of sales, WekaIO, said, “Detroit is the heart of the US automotive industry and therefore a critical territory for us as we execute our strategy to increase our dominance in this key market. We are proud to cut the ribbon on our new regional office and to welcome Mike and Tony, two seasoned players in the IT and automotive fields, to the team. Their expertise is a valuable addition to our operations and will help advance our plans for this region and industry.”
Mike Guentner holds extensive experience in sales and storage and previously served at Qumulo, Avere Systems, Compellent, and Pure Storage. Tony Raleigh, on the other hand, has a compelling background in systems engineering. Prior to joining WekaIO, he served as Systems Engineer at Qumulo and NetApp. Raleigh also has more than ten years of experience working as a consultant with specific focus on engineering systems applications for automotive suppliers.
Founded in 2014 and headquartered in San Jose, California, WekaIO provides high-performance, scalable file storage for data intensive applications. It enables companies to manage, scale, and futureproof their data center so as to help them address problems that affect the world. WekaIO Matrix, the company's flagship product and the fastest shared parallel file system in the world, advances legacy storage infrastructures by providing simplicity, scale, and the best performance density per U, for only a fraction of the cost. Furthermore, the company's NVMe-native high-performance software-defined storage solution eliminates the obstacles between the data and the compute layer, thus speeding up machine learning, artificial intelligence, genomics, research, and analytics workloads.