Maneendra Annamaneni

About Maneendra Annamaneni

I am an AWS Data Engineer with 7+ years of experience building scalable ETL and data engineering solutions on the AWS cloud. I enjoy working with data end to end—from ingestion and transformation to delivering analytics-ready datasets that create real business value. My core strengths include AWS Glue, S3, Lambda, Redshift, PySpark, and SQL, along with migrating legacy ETL systems such as Informatica to modern cloud architectures. I am highly focused and disciplined in my work, with a strong sense of ownership and accountability for the solutions I deliver. I believe in building data pipelines that are not only functional but also reliable, secure, and easy to maintain. I actively collaborate with cross-functional teams and take pride in solving complex data challenges with simple, efficient designs. I am deeply committed to continuous learning and professional growth. I consistently upskill myself by exploring new AWS services, improving performance optimization techniques, and staying updated with best practices in data engineering and cloud architecture. I enjoy learning new technologies, adapting to change, and applying that knowledge to improve systems and processes. My goal is to grow as a cloud data engineering professional while delivering high-quality, impactful data solutions.

Experience

1. Aws Data Engineer

   CVS Health

   Dec 2024 — Present · US

   Developed end-to-end ETL processes using AWS Glue, integrating PySpark, SQL queries to process fixed with files into Redshift DB.Orchestrated the migration of data from on-premises clusters to AWS, reducing production costs by 65% through efficient utilization of AWS services and implementation of autoscaling. Implemented a “Serverless” architecture using API Gateway, Lambda, and Dynamo DB and deployed AWS Lambda code from Amazon S3 buckets. Worked on AWS Athena query service that makes it easy to analyze the data in Amazon S3 using standard SQL. Built PySpark scripts for large-scale data extraction, transformation, and loading from Flat files to Redshift. Used AWS Step Functions to orchestrate multi-step ETL pipelines. Developed strategies in handling datasets using partitions, Spark SQL, broadcast joins performance tuning. Built monitoring tools for job failures and latency alerts via CloudWatch and SNSActively participated in DevOps activities using Code Commit, Code Build, and CloudFormation for automated deployments.Automated ETL/ELT glue jobs using AWS Glue workflow using s3 triggers and Lambda and Step Functions. Developing as well as modifying existing Data Flows for enhancements of new business requirements flows to load into staging tables and then to target tables in DW. Worked on various AWS tools, This includes data warehousing solutions like Amazon Redshift, data storage services such as Amazon S3, data transformation and ETL tools like AWS Glue, data visualization and reporting tools such as Amazon QuickSight, and machine learning platforms like Amazon SageMaker.• Used JIRA and Confluence for agile tracking and collaboration.• Developing trigger-based scripts for immediate job execution upon receiving triggers, using shell scripting for automation.Have experience on Airflow, Autosys and Atomic for job sceheduling.

Education

• Campbellsville University

  Master of Science - MS, Computer Science

• Audisankara College of Engineering & Technology, Gudur

  Bachelor of Technology - BTech, cse

  2016 — 2020