Yankun Song
About Yankun Song
Yankun Song is a software developer with a strong educational background in engineering and computer science, holding multiple master's degrees from Columbia University and Westcliff University. He has experience in full-stack development, data infrastructure, and AWS services, and currently works at FINRA.
Work at FINRA
Yankun Song has been employed at FINRA as a Software Developer since 2022. In this role, he collaborates with cross-functional teams to implement and maintain data infrastructure. His responsibilities include developing and maintaining Jenkins pipelines to optimize continuous integration and continuous deployment (CI/CD) processes, which enhances overall development efficiency.
Education and Expertise
Yankun Song holds multiple degrees in engineering and computer science. He earned a Master of Science in Materials Engineering from Columbia University in the City of New York, studying from 2018 to 2020. He then completed a Master of Science in Computer Science at Westcliff University from 2021 to 2022. Additionally, he studied Business Analytics at Fordham Gabelli School of Business from 2020 to 2021. He also holds a Bachelor's degree in Engineering from Southeast University, where he studied from 2013 to 2017.
Professional Experience
Prior to his current position at FINRA, Yankun Song gained valuable experience in various roles. He worked as a Full-stack Developer at Contractual for seven months in 2022. He also served as a Research Assistant at Fordham Gabelli School of Business from 2020 to 2021. Additionally, he worked as a Frontend Developer at Boston Software Group Inc. for three months in 2021.
Technical Skills and Contributions
Yankun Song has demonstrated proficiency in various technical areas. He has provisioned and configured AWS services such as Lambda, SQS, S3, EMR, and ECR to support internal applications. He has also automated data processing workflows using scripting languages like Python and shell scripts. Notably, he implemented a 30% reduction in running time for EMR clusters, leading to significant annual cost savings.