About

Anand Sharma is a highly experienced Director of Engineering with a focus on big data technologies. With over 14 years of experience in data engineering, analytics, and big data technologies such as Spark, Kafka, Hadoop ecosystem, stream processing, and NoSQL systems, Anand has become an expert in building data strategies and designing data platforms from scratch. Anand has a technical excellence in architecting and leading implementation efforts for end-to-end big data applications and frameworks. He is proficient in building high-scale analytics systems and has hands-on experience with data engineering paradigms and distributed systems. Anand has led multiple data engineering and big data application projects from scratch to production deployment. Anand's core specialties include building and managing teams of data engineers, building data strategies, and establishing data verticals to achieve the same. He has a talent for defining data roles, hiring, and designing charters for data teams to align with company goals. Anand's expertise also includes big data and analytics platforms, ETL frameworks, batch processing pipelines, and streaming applications. He has experience in designing distributed systems and scalable applications and has worked with Spark, Hadoop, Kafka, NoSQL, RDBMS, and Spark streaming systems. Currently, Anand is the Director of Engineering at Paytm. Previously, he was the Head of Data Platforms & Analytics at cult.fit, where he managed a team of 15 data engineers and analysts and contributed to the data journey of the company. He also worked as the Head of Data Initiative/Services at xto10x, where he helped startups around India and the SEA region build a functioning, scalable data vertical from scratch. Anand holds a Master of Technology in Computer Science from the Indian Institute of Technology, Madras, and a Bachelor of Engineering in Computer Science from Rajiv Gandhi Prodyogiki Vishwavidyalaya. His relevant experience is 11.95 years, and he is skilled in Senior Software Engineering, Big Data, Data Engineering, Spark, NoSQL, Hadoop, Kafka, ETL, and Data Warehousing.