Mark Khaitman
About Mark Khaitman
Mark Khaitman is a Lead Software Engineer specializing in data at FreeWheel in New York, New York, with extensive experience in data engineering and cloud architecture. He has held various roles in notable companies, including Rubicon Project and Slalom, and holds a Bachelor of Applied Science in Computer Engineering from the University of Waterloo.
Work at FreeWheel
Mark Khaitman has been serving as the Lead Software Engineer, Data at FreeWheel since 2021. Located in New York, New York, he has contributed to the company's data engineering initiatives for three years. His role involves utilizing advanced data orchestration and processing techniques to enhance FreeWheel's capabilities in managing large-scale data operations.
Previous Experience in Data Engineering
Prior to joining FreeWheel, Mark held several positions in data engineering. He worked at Rubicon Project as a Systems/Data Engineer in the Buyer Cloud from 2014 to 2016. He then transitioned to Slalom, where he served as a Senior Data Engineering Consultant from 2017 to 2020, and later as a Solution Architect from 2020 to 2021. Additionally, he gained experience at Paytm Labs as a Data Engineer from 2016 to 2017 and at Ontario Ministry of Finance as a Database Developer in 2009.
Education and Expertise
Mark Khaitman earned a Bachelor of Applied Science (BASc) in Computer Engineering from the University of Waterloo, completing his studies from 2008 to 2013. His educational background provides a strong foundation for his expertise in AWS Architecture Development, where he has worked with services such as Kinesis Streams, Glue, Lambda, and DynamoDB.
Technical Skills and Tools
Mark specializes in processing billions of events per day using various AWS services and data engineering tools. He employs Databricks and Spark Streaming for data orchestration and processing tasks. Additionally, he utilizes CloudFormation and Jenkins for infrastructure as code and continuous integration/continuous deployment (CI/CD) processes, focusing on optimizing performance and cost efficiency in data projects.