Sowmya Gandhamaneni

• Analyzed large-scale gaming and transactional datasets using SQL, Python, and Tableau to identify trends, KPIs, and performance drivers supporting strategic decision-making.
• Designed and maintained Tableau and Power BI dashboards delivering real-time business intelligence, financial analysis, and KPI monitoring for leadership teams.
• Built scalable ETL pipelines and data models using Azure Synapse, Databricks, and cloud data warehouses to support analytics and reporting needs.
• Applied machine learning, statistics, forecasting, and anomaly detection techniques to evaluate player behavior, revenue trends, and operational performance.
• Partnered cross-functionally with product management, engineering, and business stakeholders to support product enhancement initiatives through data insights.
• Developed standardized KPI frameworks, data visualization standards, and reporting best practices to improve consistency and data-driven decision-making.
• Ensured data quality, governance, compliance, and validation across analytical workflows through audits, documentation, and automated checks.
• Executed end-to-end data analytics and business intelligence workflows using SQL, Python, and BI tools to support executive reporting and operational decision-making.
• Designed scalable data warehouse and data modeling structures, enabling historical trend analysis, KPI benchmarking, and forecasting across gaming and revenue datasets.
• Partnered cross-functionally with product management and engineering teams to support product enhancement analysis through metrics, performance indicators, and statistical evaluation.

Environment: SQL, Python, Tableau, Power BI, Azure Synapse, Databricks, Azure ML, BigQuery, Excel, ETL, Data Modeling, Machine Learning, KPIs, BI Tools, Agile

• Delivered enterprise Power BI dashboards and data visualizations supporting clinical KPIs, operational efficiency, and healthcare analytics initiatives.
• Extracted, transformed, and modeled healthcare datasets using SQL, Power Query, ETL pipelines, and Azure Data Factory for accurate reporting.
• Applied Python and R statistical models to analyze patient outcomes, risk patterns, and performance indicators.
• Built reusable data models, KPI definitions, and metric frameworks aligned with healthcare compliance and reporting standards.
• Automated refresh schedules and reporting workflows, improving data reliability, decision-making speed, and operational visibility.
• Collaborated cross-functionally with clinical, IT, and leadership teams to translate business requirements into analytical solutions.
• Performed detailed data quality audits, validation checks, and documentation to ensure compliance and data accuracy.
• Built enterprise business intelligence solutions using SQL, Excel, and Power BI to support clinical operations, compliance reporting, and leadership dashboards.
• Created optimized data models and ETL pipelines supporting scalable analytics across patient, clinical, and operational datasets.
• Applied statistical analysis and machine learning techniques to evaluate patient outcomes, risk metrics, and predictive indicators.

Environment: Power BI, SQL, Power Query, Azure Data Factory, Python, R, ETL, Data Modeling, Excel, Healthcare Data, KPIs, Compliance, Agile

• Analyzed insurance claims, underwriting, and financial datasets using SQL, Python, and BI tools to support risk and fraud analytics.
• Designed Power BI dashboards visualizing KPIs, fraud indicators, financial analysis, and performance trends for business users.
• Developed optimized ETL processes, stored procedures, and data warehouse models to improve processing efficiency and reporting accuracy.
• Applied statistical analysis, anomaly detection, and trend analysis to identify emerging risks and operational inefficiencies.
• Partnered with actuarial, finance, and CRM teams to deliver cross-functional analytics and decision-support insights.
• Automated reporting pipelines and refresh schedules to support end-to-end business intelligence workflows.
• Translated complex insurance data into simplified, executive-ready visualizations supporting data-driven decision-making.
• Conducted deep data analytics and financial analysis on insurance datasets to support underwriting, risk assessment, and loss mitigation strategies.
• Developed data warehouse-friendly ETL pipelines using SQL and stored procedures to enable scalable reporting and analytics.
• Designed BI dashboards and KPI frameworks supporting actuarial, finance, CRM, and executive decision-making processes.

Environment: SQL Server, Power BI, ETL Pipelines, Data Warehouse, Stored Procedures, Python, Excel, Financial Analysis, KPIs, Insurance Data, Agile.