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.• Work independently and self-manage tasks to meet project goals.Collaborate with Business Partners:• Engage with business partners to understand their data needs and translate them intotechnical requirements.• Provide regular updates and communication to stakeholders.• Ensure data integrity and quality through rigorous testing and validation.• Implement data governance practices to maintain data security and compliance

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.• Generated Spark applications in Azure Databricks that utilize in-memory processing for complex data transformations, achieving a40% decrease in processing time compared to traditional batch processing techniques.• Crafted and modified complex SQL queries, leveraging indexing and query optimization techniques to enhance database efficiency,leading to a 30% improvement in application response time.

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 business intelligence dashboards.• 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.• Created a Power BI dashboard to visualize key business KPIs, resulting in a weekly time savings of 1 hour on manual reporting.

Master'S Degree, Computer Science

Bachelor'S Degree, Electronics & Communication Engineering