Amiya prakash Khilar

Architected and implemented data extraction pipelines using Python from Jira, ALM, QTEST to MS SQL Server and Postgres databases, leveraging Task Scheduler for scheduled runs, achieving $1000 in annual cost savings for the client through enhanced efficiency. Transformed Jira data using Informatica and performed ETL processes in the client database, reducing data processing time by 50%. Reduced man

Experienced Data Analyst with over 2+ years of proven success in the technology industry, adept at leveraging expertise in ETL processes using tools like Informatica and Azure Data Factory. Proficient in Python, SQL, Power BI, and Excel, with a track record of driving effective decision-making and problem-solving.

Automated data migration from AWS S3 to Snowflake, performed EDA using a Jupyter notebook and automated it using Jupyter Scheduler, and integrated the final report to Power BI to streamline data analysis and reporting workflows, reducing data processing time by 50%. Conducted retail data analysis using Python to identify trends, patterns, and opportunities in customer demographics, campaign perfor

Customized a multi-functional Power BI dashboard to analyze AtliQ hardware's sales trends over the global market by gathering data from 2 different sources (Excel/CSV files and a SQL database). Built data model and visualizations in Power BI, and optimized the report with DAX studio which improved the performance of the report by 5%. The dashboard enabled AtliQ hardware to analyze different depart

Generated a comprehensive P&L report for AtliQ Hardware FY 2022, utilizing advanced features in Excel such as Pivot Tables and DAX to provide granular insights into revenue streams and expense categories. Automated the data cleaning using Power Query, which can rescue around 4 hrs. of repeated manual work.

Queried complex Joins to generate the reports for sales and finance of AtliQ Hardware for (FY 17 – 22). Designed custom SQL functions, optimizing database operations and enhancing query efficiency by 15% for streamlined data processing and analysis. Optimized database operations by designing and implementing high-performing SQL procedures, significantly reducing data retrieval time from 15 seconds