Mar 2023 – Present · 3 yrs 3 mos · Chennai, Tamil Nadu, India · Hybrid

• Worked for US based Financial Service Client. Working for Insurance based US Client.
• Experience in Developing, Debugging, and Unit Testing Python Codebases, used debugger tools like breakpoint. Used Github Copilot for everyday task.
• Proficiency in Relational Databases including SQL Server, PostgreSQL, and OracleDB.
• Basic understanding of CI/CD and Experience with tools like GitHub Actions!
• Hands-on experience with AWS Services Like S3, Redshift, Lambda, DMS, Glue, Step Function, EC2 etc.
• Developed cron jobs and schedulers using Java and Spring Boot to automate migration of on-premise SQL Server data to PostgreSQL, reducing manual effort by 80% and ensuring seamless, timely data availability for downstream systems.
• Contributed to the complete Software Development Life Cycle, including requirements gathering, design, and development. Provided Knowledge Transfer and mentorship to two junior team members.
• Drove successful project completion through effective Agile collaboration with cross-functional stakeholders, while managing deliverables and communication directly with Global clients to ensure timely and strategic business delivery.
• Received Cognizant Cheers Recognition twice for consistently demonstrating an "Always Striving, Never Settling." mindset.

Mar 2022 – Jun 2022 · 3 mos · kolkata · Remote

• Mini Project 1: Designed and implemented an automated solution for starting and terminating EC2 instances using AWS Lambda and Python, optimizing resource utilization and cost efficiency.
• Mini Project 2: Deployed and managed Amazon EMR clusters to process and analyze large-scale datasets using distributed computing frameworks such as Apache Spark and Hadoop.
• Project: Developed an AWS Glue ETL pipeline to efficiently ingest and transform CSV data from Amazon S3 into Amazon Redshift tables. Automated the workflow using an AWS Lambda function written in Python to trigger data loading dynamically whenever new files matching the predefined schema are uploaded to S3.