 Utilized AWS services like S3 bucket & Redshift to manage and analyze financial data securely, resulting in 30% improvement in dataprocessing speed. Designed interactive dashboards using Tableau and Power BI, providing actionable insights to teams, leading to a 20% increase inpatient satisfaction scores. Implemented ETL processes using tools like Informatica, improving data quality and integrity by 25% across the systems. Conducted graphical analysis of Financial datasets using Python to obtain insights into Investments and returns. Designed and maintained complex SQL queries for data extraction and exploratory data analysis to identify trends such as marketanalysis and potential risks. Optimized parts of various reports like Customer Analytics, customer segment performance, credit risk and planned vs actual financial performance, boosting reporting efficiency by 15% Implemented automation and data quality checks at various stages of data processing using SQL stored procedures, python scripts, andpower query M code saving over 25% of manual efforts of the team. Collaborated with cross-functional teams using Agile methodology, resulting in an increase in project efficiency and on-time deliveryof data analytics solutions.

 Applied DAX functions to create interactive and insightful dashboards in Power BI, which enhanced data visualization capabilities andimproved stakeholder engagement by 20%. Designed and maintained ETL pipelines with AWS Glue and Apache Airflow, enhancing data integration processes and achieving a35% increase in ETL performance. Improved data models and cleansed data using SQL and Excel, ensuring high-quality data for projects. Developed SQL scripts, views, triggers, and stored procedures to update and maintain databases, for supporting operations. Increased sales by 19%during promotions through A/B testing, using Google Analytics for insights. Utilized SSIS and Talend Open Studio for ETL tasks, delivering technical solutions to business stakeholders. Executed SQL queries to access and analyse data from the relational database for data modeling and reporting. Established a data pipeline for ingesting click stream into BigQuery, and PostgreSQL, quickly scaling from 50 to 100K clicks per day. Achieved a 27% increase in operational efficiency and a reduction in delivery time by spearheading an end-to-end supply chain projectusing SQL, Spark, and GCP. Executed a Jenkins-based CI/CD pipeline integrating Kubernetes and Docker to achieve a 40% reduction in deployment time.