Chakravarthy Narasimha
About Chakravarthy Narasimha
Chakravarthy Narasimha is a Data Engineer with extensive experience in big data technologies. He has worked at notable companies including Datametica Solutions and Tata Consultancy Services, and currently holds a position at ThoughtWorks.
Work at ThoughtWorks
Chakravarthy Narasimha has been employed at ThoughtWorks as a Data Engineer since 2019. In this role, he focuses on developing data solutions and engineering practices that enhance data processing capabilities. His work involves collaborating with cross-functional teams to deliver high-quality data products and services. ThoughtWorks is known for its commitment to agile software development and innovative technology solutions, providing an environment that fosters technical growth and collaboration.
Previous Experience at Datametica Solutions
Before joining ThoughtWorks, Chakravarthy Narasimha worked at Datametica Solutions Private Limited as a Big Data Engineer from 2018 to 2019. During his tenure, he designed and implemented a data pipeline to process semi-structured sensor data from over 9 million devices. This project involved handling more than 200 million events per day, showcasing his ability to manage large-scale data processing tasks effectively.
Experience at Tata Consultancy Services
Chakravarthy Narasimha served as a Hadoop/Spark Developer at Tata Consultancy Services from 2015 to 2018. In this position, he created a tool that automated load testing of data pipelines, which was instrumental in assessing infrastructure needs by simulating data at scale. His contributions also included implementing AWS best practices, which resulted in a significant reduction of client costs on AWS by nearly 50%.
Education and Expertise
Chakravarthy Narasimha possesses a strong background in data engineering, with expertise in technologies such as Hadoop, Spark, and AWS. His education has equipped him with the necessary skills to design and implement complex data solutions. He has demonstrated proficiency in managing large datasets and optimizing data processing workflows, making him a valuable asset in the field of data engineering.