Hemal Shinde

• Prepared, cleaned, and validated datasets using Python and SQL, supporting business reporting and analytics workflows and reducing manual reporting effort by approximately 25–30%. • Built and maintained SQL queries and reporting datasets supporting 10+ KPIs and dashboards used regularly by business and product teams. • Supported deployment and release readiness of analytics pipelines and models across development, UAT, demo, and production environments, ensuring data accuracy prior to client rollout.

Coursework focused on business analytics, data analysis, and statistical methods, with an emphasis on applying data driven insights to real world business decision making.

Completed an intensive postgraduate program covering data analytics and data engineering fundamentals, including Python based data processing, SQL and DBMS concepts, data visualization, applied statistics, and introductory machine learning through practical modules and project work.

Built a strong foundation in computer engineering, covering programming, database systems, software engineering, and core computing concepts through structured coursework and academic projects.