Aparna Chinthalapudi

• Collected, cleaned, and analyzed large healthcare datasets related to patient outcomes, treatment effectiveness, and operational metrics to support data-driven decision-making processes.
• Developed and maintained interactive dashboards and reports using SQL, Python, and Tableau to track key performance indicators (KPIs) in healthcare operations.
• Designed automated ETL processes using AWS services including Lambda, S3, and Redshift to streamline data extraction, transformation, and loading from multiple healthcare systems.
• Created custom Tableau visualizations including trend analysis, heat maps, and drill-down reports to provide actionable insights into patient care and resource utilization patterns.
• Collaborated with healthcare professionals, administrators, and IT teams to analyze data requirements and deliver solutions for improving patient care and operational efficiency.
• Implemented Snowflake data warehouse solutions with optimized clustering and materialized views, reducing query processing time by 45% for critical healthcare reporting.
• Enhanced internal tracking and forecasting capabilities using Python and statistical analysis to improve decision-making on resource allocation and patient care strategies.
• Developed parameterized dashboards in Tableau allowing dynamic filtering and real-time decision-making capabilities for healthcare stakeholders.
• Built AWS Lambda functions for automated data processing workflows, reducing manual intervention and improving data processing efficiency for healthcare reporting.
• Designed data governance frameworks and implemented best practices to ensure HIPAA compliance and data security in healthcare environments.
• Created automated alerts and monitoring systems to track data quality, system performance, and compliance with healthcare regulatory requirements.
• Maintained comprehensive data dictionaries, technical documentation, and metadata repositories to ensure consistency in healthcare data reporting.
• Presented findings and recommendations to healthcare leadership and clinical teams to improve patient outcomes and operational efficiency.

Environment: Python, SQL, AWS (S3, Lambda, Redshift, Athena), Snowflake, Tableau, Power BI, Git, Pandas, NumPy

• Collaborated with ETL development teams to automate data load processes, create aggregate tables, and optimize database indexing for improved report performance.
• Developed comprehensive dashboards using Tableau and Power BI to visualize business performance, financial metrics, and operational KPIs for executive reporting.
• Designed ETL processes using Informatica PowerCenter to integrate financial data from Oracle, MySQL, and PostgreSQL databases into centralized data warehouse.
• Built data validation frameworks using SQL and Python to ensure 99% data accuracy across financial reporting systems and regulatory compliance requirements.
• Implemented AWS cloud migration strategies, transitioning on-premises data systems to S3 and Redshift infrastructure while maintaining zero downtime.
• Created custom SQL views and stored procedures involving complex multi-table joins and subqueries to support advanced financial analysis and reporting needs.
• Conducted extensive data profiling and quality assessment using advanced SQL techniques across multiple source systems to identify and resolve data inconsistencies.
• Automated recurring financial reports and data processing workflows using SQL Server Agent and Python scripts, reducing manual effort by 60%.
• Enhanced data visualization capabilities by developing interactive Power BI dashboards for product analytics and customer engagement metrics tracking.
• Designed incremental data loading strategies and staging table architectures for Snowflake implementation, optimizing data refresh performance and system resources.
• Implemented version control processes using Git for ETL code and database scripts, ensuring proper change management and deployment procedures.
• Collaborated with cross-functional teams including finance, operations, and IT to define business requirements and translate them into technical specifications.

Environment: Informatica PowerCenter, SQL, Python, AWS (S3, Redshift), Oracle, MySQL, PostgreSQL, Tableau, Power BI, Git, Snowflake, Pandas, NumPy

• Analyzed large datasets related to sales performance, customer retention, and marketing effectiveness using SQL, Python, and Excel to support business decision-making.
• Developed interactive reports and dashboards using Tableau and Power BI to track sales KPIs, customer metrics, and marketing campaign performance.
• Created automated ETL workflows to extract data from Oracle databases, flat files, and API sources, ensuring consistent data flow for business reporting.
• Implemented data validation processes and quality checks using SQL and Python to maintain accuracy in customer data and sales reporting systems.
• Designed star schema data models and dimensional structures to support efficient analytical queries and business intelligence requirements.
• Built custom Python scripts using Pandas and NumPy libraries for data cleansing, transformation, and statistical analysis of customer and sales data.
• Collaborated with sales teams, marketing departments, and executive leadership to understand reporting requirements and deliver actionable business insights.
• Automated weekly and monthly reporting processes using SQL Server Agent, reducing manual report generation time by 25% and improving data accuracy.
• Created comprehensive documentation including data dictionaries, process workflows, and technical specifications to support knowledge transfer and system maintenance.
• Performed statistical analysis and forecasting to support sales predictions and customer behavior modeling for strategic planning initiatives.
• Optimized database queries and implemented indexing strategies to improve report performance and reduce execution times for business-critical reports.
• Supported ad-hoc analysis requests from business stakeholders, providing quick turnaround on data insights and recommendations for operational improvements.

Environment: Oracle, MySQL, SQL, Python, Tableau, Power BI, Excel, Git, Pandas, NumPy, Windows Server