Kc Dyer
About Kc Dyer
Kc Dyer is a Senior Engineer I at Menlo Security Inc. in Canada, with extensive experience in network engineering and project management, specializing in blockchain technologies and various programming frameworks.
Work at Menlo Security
Kc Dyer has been employed at Menlo Security Inc. as a Senior Engineer I since 2022. In this role, Dyer contributes to the company's mission of providing secure web access and protecting organizations from cyber threats. The position involves leveraging advanced engineering skills to enhance security solutions and improve overall system performance.
Previous Experience at Swift Internet
Prior to joining Menlo Security, Kc Dyer worked at Swift Internet for nine years, holding multiple roles including Lead Network Engineer, DevOps, Full Stack Developer, and Site Reliability Engineer (SRE). This experience allowed Dyer to develop a comprehensive skill set in network engineering and software development, contributing to the company's operational efficiency and service delivery.
Technical Background and Education
Kc Dyer has a solid educational foundation in technology. Dyer completed a Diploma in Computer Systems Networking and Telecommunications at Okanagan College from 2004 to 2006. Additionally, Dyer studied Project Management Professional at the College of the Rockies in 2012. Dyer also holds a Grade 12 diploma from Prince Charles Secondary School, completed from 1996 to 2000.
Expertise in Blockchain Technologies
Kc Dyer specializes in monetizing business strategies through the use of underutilized blockchain technologies, including LoraWAN, IPFS, and Web3 Networking. This expertise enables Dyer to explore innovative solutions that enhance business operations and drive technological advancements in various sectors.
Specializations and Technical Skills
Kc Dyer possesses a range of technical skills, specializing in PHP/MVC Frameworks, xHTML, MySQL, PostgreSQL, NodeJS, and Solidity. Dyer is experienced in creating long-term, time-based relational tables for aggregating statistics over various scaling intervals, which supports data analysis and decision-making processes.