Shashidhar Reddy Reddy Gun Reddy

• Senior Azure Data Engineer with 6+ years of experience designing and building scalable data solutions, including data warehousing and data streaming platforms, to support product data foundations. • Expertise in data transformation, Apache Spark, and Python, with hands-on experience using Databricks to implement and optimize ETL pipelines and complex data transformations for large-scale datasets. • Strong background in optimizing data workflows, performance tuning, and troubleshooting, ensuring data accuracy, consistency, and security across systems while coding in Scala or Python. • Skilled in collaborating with cross-functional teams to define data requirements and deliver business insights, leveraging strong problem-solving, analytical, and communication skills. • Proactive problem-solver with a focus on continuous learning and innovation, proven ability to design and implement complex data transformations using Python Spark libraries (e.g., PySpark). • Designed and deployed scalable data ingestion pipelines using Microsoft Azure Cloud, PySpark, and Python, contributing to reliable, high-performance data solutions for various business needs. • Transitioned on-premises solutions to Azure Cloud, ensuring seamless data migration and improved performance, while maintaining a solid knowledge of Apache Spark and distributed data processing. • Developed ETL workflows using Informatica PowerCenter and Azure SQL for efficient data integration, ensuring data accuracy and consistency across diverse data warehousing platforms. • Implemented data migration strategies for SQL Server, MySQL, Oracle, and PostgreSQL databases to Azure, leveraging strong problem-solving and analytical skills to ensure data integrity. • Integrated Kafka with Azure Databricks for real-time streaming data analysis, contributing to the development of high-performance data solutions and improved data workflows. • Designed data models in Azure Synapse Analytics for self-service analytics and business intelligence, ensuring data accuracy and consistency across various data streaming platforms. • Developed and optimized queries using BigQuery SQL and T-SQL for efficient data retrieval, leveraging Python Spark libraries (e.g., PySpark) for data querying and transformations. • Integrated Hive, Spark, and external systems like Kafka, Elasticsearch, and HBase for advanced analytics, contributing to the design and implementation of complex data transformations. • Orchestrated complex workflows using Apache Airflow, Kubernetes, and Azure DevOps, ensuring data accuracy,