• Led the design, development, and maintenance of sophisticated data solutions for complex analytics, enhancing business intelligence and data driven strategies for products and services.• Utilized data processing techniques using Python and PySpark to manage and process large volumes of data, achieving a 30% improvement in data reliability and analytical accuracy. This significantly boosted data-driven decision-making processes. • Applied advanced data mining techniques to identify patterns and correlations, increasing model accuracy by 25%. This provided critical insights that propelled strategic business initiatives and fortified competitive advantage.• Developed and enhanced interactive dashboards and reports using Matplotlib, Seaborn, and Plotly, which enhanced stakeholder engagement and improved decisionmaking efficiency through more accessible and clearer data presentations.• Engineered and maintained robust data pipelines using Azure Data Factory, improving the efficiency of data flow from source to analysis by 35%. This ensured faster turnaround times for data availability and analytics readiness.• Implemented and optimized a cloud-based data warehouse using Azure Synapse Analytics, which boosted performance by 20% and cut operational costs by 10%, supporting more scalable and cost-effective analytics solutions.

Contributed to multiple projects, specializing in data tasks. Led the setup of an End-to-End Data Pipelines, overseeing diverse use cases. Built Azure Data Factory pipelines using PySpark, SQL, and Databricks for efficient data loading. Additionally, played a key role in creating Data Requirements Documents and Data Architecture Designs. Utilized Azure Synapse Analytics to extract business insights. Overall, contributed to project success through strategic data management and analytics.