Justin Kraus
About Justin Kraus
Justin Kraus is a Data Engineer at Condé Nast, where he has worked since 2021. He holds a Bachelor of Science in Finance and Entrepreneurship from Northeastern University and a Master of Science in Data Visualization from Parsons School of Design.
Work at Condé Nast
Justin Kraus has been employed at Condé Nast as a Data Engineer since 2021. He operates within the New York City Metropolitan Area and has accumulated three years of experience in this role. His responsibilities include managing infrastructure and microservice applications using AWS, as well as developing data pipelines with Databricks to transform raw data into usable datasets for various consumer business areas.
Education and Expertise
Justin Kraus holds a Bachelor of Science (BS) degree in Finance and Entrepreneurship from Northeastern University, where he studied from 2004 to 2009. He furthered his education by obtaining a Master of Science (MS) in Data Visualization from Parsons School of Design - The New School, completing this program in 2021. His educational background provides a strong foundation in both financial principles and data analysis.
Background
Before joining Condé Nast, Justin Kraus worked at Bank of America Merrill Lynch in various roles. He served as Assistant Vice President from 2009 to 2014 and then held the position of Assistant Vice President in Global Banking and Capital Markets for one year from 2014 to 2015. Following this, he worked at Barclays Investment Bank as Vice President in the Capital Planning and Analysis Program from 2015 to 2021.
Achievements in Data Engineering
In his role as a Data Engineer, Justin Kraus has focused on optimizing scalable automated software systems for processing both structured and unstructured data. His work includes managing infrastructure and microservice applications on AWS, which enhances the efficiency and effectiveness of data handling within the organization. He has also developed data pipelines using Databricks, contributing to the transformation of raw data into actionable datasets.