Naga Sai Rishindra Popuri Email & Phone Number

• Create and manage ETL processes to extract, transform, and load data from various sourcesinto data warehouses.
• Optimize data pipelines for performance and reliability.
• Implement and manage integrations using Mulesoft to ensure data flows efficientlybetween systems.
• Design and manage data warehouses using AWS Redshift.
• Utilize various AWS tools (e.g., S3, Lambda, Glue, RDS) for data storage, processing, andtransformation.
• Take projects from conception to completion, ensuring timelines and quality standards aremet.

Texas, United States

• Designed and implemented various Airflow DAGs (Directed Acyclic Graphs) to automate data pipelines, resulting in a 20%reduction in manual data processing tasks.
• Established real-time fraud detection pipelines using Spark Streaming and Scala to analyze high-volume transaction data, enablingimmediate identification and prevention of fraudulent activity.
• Leveraged Databricks as a cloud-based platform for deploying and managing Apache Spark workloads, enabling efficient resourceallocation and scalability for big data processing.
• Built data integration pipelines using Azure Data Factory to automate data movement and transformation between various datasources and sinks, reducing manual effort by 10%.
• Designed and implemented high-performance data warehouses on Snowflake for efficient storage, querying, and analysis of largeand growing datasets, enabling scalability for future data needs.
• Applied data lake in Azure Data Lake Storage, improving data accessibility and collaboration for data scientists and analysts.

India

• Increased data processing efficiency by 80% by migrating complex data transformations from AWS services to Azure Databrickswithin the data pipeline.
• Orchestrated data pipelines using Airflow to automate data ingestion, transformation, and loading tasks between various datasources (Kafka, HDFS) and data warehouses, ensuring reliable and scheduled data delivery for.
• Enhanced Spark jobs to achieve 30% faster processing times by utilizing techniques like partitioning, data caching, and codeoptimization.
• Implemented materialized views and partitioning strategies in AWS Redshift to improve query performance by 15%.
• Built ETL pipelines in AWS Glue using PySpark to transform and load data into Redshift, reducing processing time by 25%.
• Executed serverless data pipelines using AWS Lambda to process real-time data streams, achieving a 10% reduction in processinglatency compared to traditional batch processing methods.

Master'S Degree, Computer Science

Bachelor'S Degree, Electronics & Communication Engineering