Sravanthi Batthula

• Designed and executed complex data transformation processes, including data cleansing, normalization, and enrichment, ensuring data quality and consistency
• Implemented data ingestion pipelines using Azure Data Factory to efficiently collect and transport data from various sources into Azure data repositories
• Implemented data quality checks and validation processes during data migration, resulting in a 75% reduction in data errors and ensuring the accuracy and reliability of migrated data within Azure environments
• Developed and maintained data models within Azure Data Lake Storage and Azure SQL Data Warehouse, optimizing schema structures for efficient querying and reporting
• Built and managed ETL (Extract, Transform, Load) workflows to automate data processing and orchestration, resulting in improved data integration and reduced manual intervention
• Utilized Azure Databricks and Apache Spark for processing large-scale datasets, enabling advanced analytics and machine learning applications
• Demonstrated PySpark within Azure Databricks to process and analyze large-scale datasets, applying advanced data transformations, aggregations, and machine learning algorithms to derive meaningful insights and support data-driven decision-making
• Designed and implemented scalable and efficient data warehousing solutions using Azure Synapse Analytics (formerly SQL Data Warehouse), optimizing schema structures and distribution keys to support complex analytical queries, resulting in improved data retrieval performance
• Implemented data storage optimization techniques within Azure data repositories, resulting in a 60% reduction in storage costs while maintaining data availability and performance
• Proficiently managed NoSQL databases within Azure, including Azure Cosmos DB and Azure Table Storage, ensuring high availability, scalability, and performance of non-relational data storage solutions

Designed and executed complex data transformation processes, including data cleansing, normalization, and enrichment, ensuring data quality and consistency
Implemented data ingestion pipelines using Azure Data Factory to efficiently collect and transport data from various sources into Azure data repositories
Implemented data quality checks and validation processes during data migration, resulting in a 75% reduction in data errors and ensuring the accuracy and reliability of migrated data within Azure environments
Developed and maintained data models within Azure Data Lake Storage and Azure SQL Data Warehouse, optimizing schema structures for efficient querying and reporting
Built and managed ETL (Extract, Transform, Load) workflows to automate data processing and orchestration, resulting in improved data integration and reduced manual intervention
Utilized Azure Databricks and Apache Spark for processing large-scale datasets, enabling advanced analytics and machine learning applications
Demonstrated PySpark within Azure Databricks to process and analyze large-scale datasets, applying advanced data transformations, aggregations, and machine learning algorithms to derive meaningful insights and support data-driven decision-making
Designed and implemented scalable and efficient data warehousing solutions using Azure Synapse Analytics (formerly SQL Data Warehouse), optimizing schema structures and distribution keys to support complex analytical queries, resulting in improved data retrieval performance
Implemented data storage optimization techniques within Azure data repositories, resulting in a 60% reduction in storage costs while maintaining data availability and performance
Proficiently managed NoSQL databases within Azure, including Azure Cosmos DB and Azure Table Storage, ensuring high availability, scalability, and performance of non-relational data storage solutions

Designed and implemented complex data models using DAX, enhancing data accuracy and enabling efficient analysis across multiple business units
Worked closely with clients to understand their requirements, translating business needs into dynamic visualizations that provided actionable insights and increased user engagement
Created executive-level Power BI dashboards that provided summarized views of company-wide metrics, facilitating informed high-level decision-making
Seamlessly integrated diverse data sources, including cloud-based databases, APIs, and on-premises systems, enabling real-time data updates for stakeholders
Employed Power Query extensively to clean and transform raw data from various sources, ensuring accurate and consistent reporting
Implemented automated report generation using Power BI's scheduling capabilities, reducing manual effort by 50% and enabling timely distribution of insights
Crafted optimized SQL queries to extract data from relational databases, improving data retrieval speed and accuracy within Power BI reports
Enforced data governance practices by implementing standardized naming conventions and data definitions, enhancing data consistency across reports
Developed tailored Power BI solutions aligned with business objectives, enabling stakeholders to monitor key performance indicators and track progress