Sree Charan Email & Phone Number

United States

Orchestrated seamless data flow using Azure Data Factory, ADLS Gen2, and Databricks with PySpark for scalable transformations.Mastered Data Flows and other Azure Data Factory features to optimize ETL processes, ensuring data cleansing, filtering, and aggregation for advanced business insights.Migrated and optimized legacy data warehouse to Snowflake on Azure, achieving 3x faster query performance and 40% reduction in data storage costs.Applied advanced performance tuning techniques in Azure Data Engineering, boosting query performance by 40% in Azure Databricks through proactive measures.Optimized data ingestion via historical, incremental, and full load pipelines, ensuring efficient data storage, accurate updates, and real-time responsiveness.Implemented Azure Event Hub for real-time data ingestion in retail, improving foot traffic monitoring, fraud detection, and operational agility.

United States

Designed and implemented scalable data ingestion pipelines with Azure Data Factory, efficiently handling diverse data sources from On-Prem, including SQL databases, CSV files, and REST APIs. Developed robust data processing workflows with Azure Data bricks and Spark, ensuring comprehensive data validation, cleansing, and transformation.Identified and resolved performance bottlenecks within data processing and storage layers, applying advanced techniques such as partitioning, indexing, and caching in Snowflake and Azure services. Implemented scalable real-time data pipelines using Kafka and Azure Event Hubs, achieving near-instantaneous analytics and a 99.9% uptime SLA. Leveraged Kafka and Spark Streaming for processing and analysing streaming data, improving real-time analytics capabilities by 30%.

United States

Designed and implemented a scalable ETL framework using SQOOP, Pig, and Hive for efficient data extraction, transformation, and loading. Developed robust ETL jobs with Spark and Scala, ensuring seamless migration of data from Oracle to MySQL while maintaining data integrity. Processed data stored in Hadoop Distributed File System (HDFS), leveraging Hive for creating external tables and developing reusable scripts for efficient table ingestion and repair. Utilised Spark's powerful capabilities, including RDDs, Data Frames, and Spark SQL, along with Spark-Cassandra Connector APIs, for diverse data tasks such as migration and generating comprehensive business reports.Expertly configured and managed Zookeeper for coordination and synchronisation in distributed data environments.Employed advanced techniques like combiners, partitioning, and distributed cache to optimise the performance of MapReduce jobs.

United States

Mastered data integration in OLTP/OLAP environments, excelling in building high-performance ETL pipelines with SQL Server and SSIS. Demonstrated expertise in monitoring and optimizing SQL Server performance using industry best practices, ensuring the efficiency and high performance of database systems.Designed ETL data flows using SSIS, showcasing proficiency in developing mappings and workflows for seamless data extraction from SQL Server. Contributed to the end-to-end Business Intelligence solution by creating shared dimension tables, measures, hierarchies, cubes, and aggregations on MS OLAP/Analysis Server (SSAS). Designed and deployed complex ETL packages (SSIS) to pull data from Oracle and load monthly data from Excel to SQL Server.

Masters In Computer Science, Computer Science

Bachelor Of Engineering - Be, Computer Science