• Independently designed, built, and maintained ETL pipelines using Python and REST APIs on AWS.Implemented the boto3 library for AWS service automation, reducing manual effort and improving efficiency.• Designed and developed REST APIs to extract data from various sources and transform it into a standardized format for further processing.• Utilized AWS Glue for data orchestration, reducing cost and ensuring data integrity.• Automated the loading of transformed data into AWS Redshift, improving data warehousing capabilities.Enhanced data pipeline processing speed by leveraging AWS Lambda functions, Step functions, and parallel processing techniques.• Implemented error handling and logging to improve data accuracy and provide visibility into data quality.• Developed a monitoring and reporting system, providing real-time updates on data pipeline status.• Mentored junior team members on best practices for designing and implementing ETL pipelines.

• Collaborated with a team of data engineers and data scientists to develop end-to-end machine learning pipelines from data collection to prediction.• Implemented data cleaning and feature engineering techniques using PySpark to process large-scale datasets efficiently.• Utilized Google Cloud Platform tools such as Google Cloud Storage, Google Cloud Dataproc, and Google Cloud Airflow to orchestrate and automate data workflows.• Implemented automation project using Python shell jobs and GitHub Actions to create and manage Google Cloud resources, reducing manual effort and enhancing operational efficiency.• Utilized GitHub Actions workflows to automate resource creation upon code pushes, ensuring consistency and improving deployment speed.