Roopak Madhu
About Roopak Madhu
Roopak Madhu is a Lead Data Engineer at Immutable, where he has worked since 2022. He has extensive experience in data engineering and governance, having held various roles at companies like ING, Afterpay, Assembly Payments, and Infosys over more than a decade.
Work at Immutable X
Roopak Madhu has been serving as the Lead Data Engineer at Immutable X since 2022. In this role, he is responsible for overseeing data engineering projects and ensuring the effective management of data systems. His work involves leveraging advanced data solutions to support the company's objectives in the blockchain space.
Previous Experience in Data Engineering
Before joining Immutable X, Roopak held several positions in data engineering. He worked at ING as a Chapter Lead/Data Engineer from 2016 to 2018. Following that, he was employed at Afterpay as a Data Engineer from 2018 to 2019 and later as a Lead Data Engineer from 2019 to 2020. He also served as Data Lead at Assembly Payments from 2020 to 2022.
Career Background at Infosys
Roopak began his career at Infosys, where he held multiple roles over several years. He worked as a Systems Engineer from 2008 to 2011, advancing to Technology Analyst from 2012 to 2014, and then as Technology Lead from 2014 to 2016. He also served as a Senior Systems Engineer for 11 months in 2011.
Technical Skills and Expertise
Roopak possesses strong technical skills in data solution architecture, focusing on the design and implementation of data systems. He is proficient in reporting tools such as Looker and Tableau for data visualization. His expertise extends to data governance, quality, and management, ensuring high standards in data processing. Additionally, he has experience with Amazon Web Services (AWS), including Amazon Redshift and Snowflake.
Automation and CI/CD Experience
Roopak has experience in building, testing, and deploying automation processes, particularly within Continuous Integration and Continuous Deployment (CI/CD) environments. This expertise allows him to streamline data workflows and improve operational efficiency in data engineering projects.