Sai Tejaswini Thumpala

• Implement data ingestion, data transformation, data analytics, data storage, and database management using Azure Databricks and Azure SQL Database and work with 10 TB of data in Azure Data Factory
• Perform data integration, ETL processes, data modeling in Azure Synapse Analytics and make sure data loads pass through all the test cases in DEV, SIT, UAT, and PROD environments
• Architected robust data models that supported business intelligence insights and optimized Azure SQL database performance, cutting storage costs by 22%, without compromising on speed
• Analyzes and processes various components like mapping data flows within Data Factory and supports low-latency workloads in Data Lake Storage and facilitates high-performance processing which reduces process time by 40%. Collaborate with team to develop Databricks notebooks as per requirements in Azure DevOps

• Provided ETL development and generated SQL scripts in Oracle to produce transaction reports. Extracted 5 million records of relevant data from on-premises data source and automated process using stored procedures
• Built dimensional data models for client data and utilized Agile methodologies to keep track of stories and sprint progress. Executed system architecture for Azure Data Factory based solution for client and performed complex SQL queries to analyze financial data, increasing actionable insights by 35%

• Designed and implemented the efficient data pipeline to process the customer data using Python and optimized the queries using SQL and provided insights through the visualization dashboards and cooperated closely with the production team to build Machine Learning model that resulted in 16% increase in sales
• Operated a team of three interns and produced documentation including detailed analysis, error tracking, and data management, and built 50+ Tableau dashboards saving 15 hours of manual reporting work each week

• Analyzed international students' data and performed weekly data visualization tasks using filters and hyper parameter tuning and decreased application time by 31% with porting code using Python

• Performed pre-processing of 1M+ rows data and ingested high-volume data across different data sources using Python, and integrated Power BI dashboards to perform and visualize Data analytics results

· Provide data-driven insights for product development, leading to 15% growth in customer engagement in 6 months and coordinate with cross-functional teams to understand key metrics and data requirements

· Created dynamic dashboards and interactive charts using Power BI and Excel pivot tables to present key insights, automated tasks by reducing manual work by 60%. Supported product roadmaps and monitor product performance and usage metrics to identify areas of improvement