Amit Tiwary
About Amit Tiwary
Amit Tiwary is a Senior Big Data Analyst at FINRA, where he has worked since 2020. He specializes in migrating surveillance patterns for the Consolidated Audit Trail (CAT) project and has extensive experience in data analysis using SQL, Python, and PySpark.
Work at FINRA
Amit Tiwary has been employed at FINRA as a Senior Big Data Analyst since 2020. In this role, he focuses on migrating and prototyping surveillance patterns for the Consolidated Audit Trail (CAT) platform. This platform is designed to handle over 100 billion market events daily. He is responsible for analyzing complex business logic and technical implementation, as well as creating Business Requirement Documents (BRDs) to support development efforts.
Education and Expertise
Amit Tiwary holds a Bachelor of Engineering (B.E.) in Metallurgical Engineering from the Indian Institute of Technology, Roorkee, which he completed in 1996. He also earned a Bachelor of Science (B.S.) in Marine Transportation/Nautical Sciences from the University of Mumbai in the same year. Additionally, he obtained a Master of Business Administration (MBA) in Finance and Operations Management from the University of Rochester - Simon Business School between 2008 and 2010. His expertise includes data analysis, SQL, Python, and PySpark, particularly in a Big Data environment.
Professional Background
Before joining FINRA, Amit Tiwary held various positions in the field of data analysis and business systems. He worked at Xerox as a Supplier Manager - Transportation from 2009 to 2011 and later as a Business Systems/Data Analyst from 2014 to 2017. He also served as a Technical Business Analyst at JPMorgan Chase & Co. from 2011 to 2014. Prior to these roles, he was a Senior Data Analyst at Hilton from 2018 to 2020 and worked as an Operations & Logistics Officer in the merchant marine sector from 2000 to 2008.
Achievements in Big Data Analysis
At FINRA, Amit Tiwary has played a significant role in transitioning surveillance patterns from traditional threshold-based SQL methods to advanced Deep Learning and Machine Learning algorithms for fraud detection in stock market transactions. His contributions to the development of the CAT project highlight his capabilities in handling large-scale data environments and implementing innovative solutions.