Sathwika ManneM

Developed interactive Power BI dashboards and reporting workflows using SQL, Python, REST APIs, Azure, and Amazon Redshift to track fan engagement, ticketing, sponsorship, and operational metrics for business stakeholders. Integrated and transformed data from multiple sources via automated ETL workflows to produce consistent datasets for KPI reporting and dashboard analysis. Built reusable SQL data models and Python automation scripts to improve data consistency, reporting reliability, and accessibility for non-technical teams. Automated a fan engagement rewards process using custom multi-game streak logic, reducing manual effort by 5+ hours per day while improving accuracy and operational efficiency. Partnered with Marketing, Partnerships, Finance, and Operations to define metrics and translate requirements into actionable dashboards and analytics solutions.

Analyzed datasets using Python (Pandas), Scikit-learn, and TensorFlow to study patterns and evaluate machine learning and deep learning model outcomes. Prepared and reviewed analytical results with an emphasis on reproducibility, structured experimentation, model performance assessment, and consistent interpretation of findings.

Designed and maintained ETL pipelines to integrate, clean, and transform enterprise datasets from multiple sources, improving data consistency and reporting efficiency. Conducted exploratory data analysis on sales, customer, and marketing datasets to identify trends and investigate anomalies for business reporting. Built interactive Tableau dashboards to communicate KPIs across revenue performance, campaign effectiveness, and customer/operational trends. Automated recurring reports using SQL and Python to reduce manual processing time and support dependable reporting delivery. Collaborated with business stakeholders to understand reporting requirements and convert complex data into accessible visualizations that supported performance monitoring and decision-making.