• Developed and optimized scalable data processing scripts using Python and PySpark to handle large datasets for an e-commerce platform.• Perform in-depth EDA using Pandas and other Python libraries to uncover insights and inform data-driven decisions.• Deployed data solutions on AWS, leveraging services like S3, Lambda, and EC2 to manage and process data at scale.• Manage on-premises SQL Server databases and develop SSRS reports.• Utilized Pandas for efficient data manipulation, cleaning, and transformation, preparing datasets for advanced analytics and reporting.• Validated already developed python reports, fixed identified bugs, and re-deployed the same.

• Developed and maintained efficient data pipelines using Azure Data Factory, Databricks, PySpark, and SQL, enabling large-scale data processing to support product analytics.• Conducted in-depth quantitative analysis and experimentation using Python and Pandas to uncover product insights and drive strategic recommendations.• Implement cloud-based data solutions using AWS services like Amazon S3, Lambda, and RDS, EMR, Data pipeline, AWS Glue, AWS Athena.• Build and optimize ETL processes and data models across SQL Server, MySQL, and cloud databases.• Set up CI/CD pipelines using Azure DevOps and manage version control with GIT.• Developed Power BI dashboards to monitor key metrics, enabling stakeholders to track performance trends and measure the success of efforts.• Ensured data quality, security, and compliance across platforms.• Collaborate with stakeholders to understand business requirements and translate them into technical specifications.Achievements:• Successfully reduced the ETL process time by enabling faster access to critical sales insights.• Played a key role in migrating the company’s data infrastructure to a cloud-based platform, improving scalability and reducing operational costs.