Vaishak Madhu
About Vaishak Madhu
Vaishak Madhu is an Assistant Manager in Big Data Analytics at KPMG India, with over 7 years of experience in data engineering and analytics. He has a strong background in building data pipelines using technologies such as Python, Scala, and a Big Data tech stack including Hadoop and Spark.
Work at KPMG India
Vaishak Madhu currently serves as an Assistant Manager in Big Data Analytics at KPMG India. He has been with the firm since 2019, working from Bengaluru, Karnataka. In this role, he focuses on technology risk and cloud computing within data analytics and engineering, particularly for one of the world's largest investment banks. His responsibilities include collaborating with global stakeholders to implement best practices in analytics instrumentation and ensuring the integrity of data capture.
Previous Experience at Cognizant
Before joining KPMG India, Vaishak worked at Cognizant as an Associate in Big Data Analytics from 2014 to 2019. During his five years in the Chennai Area, he gained significant experience in building data-intensive pipelines and applications. His role involved utilizing a Big Data tech stack, including Hadoop, Spark/PySpark, Hive, and Sqoop, for data processing and analytics.
Education and Expertise
Vaishak Madhu earned his Bachelor’s Degree in Computer Science from Amrita Vishwa Vidyapeethom, completing his studies from 2010 to 2014. His educational background laid the foundation for his expertise in data engineering, data analytics, and data science. With over seven years of experience in the consulting and financial services industry, he has developed strong hands-on skills in building data pipelines and applications using programming languages such as Python and Scala.
Data Engineering and Analytics Skills
Vaishak possesses extensive skills in data engineering and analytics. He specializes in identifying and ingesting data from various platforms, including HDFS and RDBMS, into data lakes. He refines and processes ingested data using Spark SQL and Hive to create risk-related measures and metrics. His technical expertise enables him to effectively handle data-intensive projects and contribute to analytics initiatives.