Riley Stockton
About Riley Stockton
Riley Stockton is a Data Engineering Manager at Swish Analytics, where he has contributed to the development of a feature engineering framework and led the implementation of scalable data pipelines. He also mentors junior data engineers and has developed training programs to enhance onboarding processes.
Work at Swish
Riley Stockton currently serves as the Data Engineering Manager at Swish Analytics, a position held since 2019. In this role, Riley has led the implementation of a scalable data pipeline that supports real-time analytics for machine learning models. Prior to this managerial role, Riley worked as a Data Engineer at Swish Analytics from 2015 to 2019. During this time, Riley contributed to the development of a feature engineering framework that enhanced the accuracy of predictive models.
Education and Expertise
Riley Stockton earned a Bachelor of Science (BS) degree in Mathematics from the University of California, Santa Cruz, completing the program from 2009 to 2014. This educational background provides a strong foundation in analytical and quantitative skills, which are essential in the field of data engineering. Riley's expertise includes mentoring junior data engineers, focusing on career development and technical skills enhancement.
Background
Riley Stockton has a background in data engineering, with significant experience in developing and implementing data solutions. Riley's career in the San Francisco Bay Area includes a four-year tenure at Swish Analytics as a Data Engineer, followed by a promotion to Data Engineering Manager. This progression reflects a commitment to professional growth and expertise in data management.
Achievements
Riley Stockton has made notable contributions to Swish Analytics, including the development of a training program designed to accelerate the onboarding process for new hires in data engineering roles. Additionally, Riley participated in a panel discussion on data engineering best practices at a major tech conference in San Francisco, showcasing knowledge and engagement with the broader data engineering community.