Benjimin Katta

 Designed and implemented ETL processes using Azure Data Factory to automate data extraction, transformation, and loading into data warehouses, enhancing data accessibility for analytical purposes.  Analyze business and financial data to generate actionable insights that drive automation, process improvements, and strategic decisions.  Design, develop, and maintain data pipelines and ETL workflows to collect, clean, and process large-scale structured and unstructured datasets.  Build and maintain interactive dashboards and visualizations using tools such as Tableau or Power BI to communicate key performance metrics and insights effectively to stakeholders.  Develop and implement predictive models (e.g., customer retention, churn prediction, propensity to convert) using machine learning algorithms and statistical techniques.  Perform rigorous statistical analysis, hypothesis testing, and experimentation to validate strategies and measure impact.

 Utilized advanced SQL for querying, cleaning, and analyzing large-scale financial datasets, ensuring high data integrity and enabling data-driven investment strategies—contributing to a 15% increase in portfolio returns.  Optimize data storage, processing, and retrieval by designing efficient database structures and writing complex SQL queries.  Utilized Excel (Power Query, PivotTables, and VBA) to automate financial modeling tasks, streamline risk assessment workflows, and support real-time data analysis for portfolio managers.  Created end-to-end ETL processes using SQL and Alteryx to automate data extraction, transformation, and loading from diverse financial data sources, cutting data processing time by 50%.  Translate technical findings into clear and actionable reports for both technical and non-technical stakeholders, ensuring alignment with business goals.  Collaborated with cross-functional teams to integrate predictive analytics and trend analysis using Python (Pandas, NumPy), complementing SQL-based insights for more accurate forecasting.  Supported cloud-based data integration using AWS S3 and Glue, while focusing primarily on SQL-driven data transformation logic to ensure scalable analytics pipelines.