Sravya Thavidishetty

Azure Data Engineer @Capgemini

CA

Developed data pipelines using Azure Data Factory (ADF) and Azure Databricks, integrating complex data sources into centralized cloud environments and ensured smooth data ingestion and transformation, while optimizing performance for real-time analytics and business intelligence applications• Refined data transformation processes in Azure Synapse Analytics, leveraging advanced SQL, Python, and Spark to streamline operations and enhance the scalability of the data platform to focus on reducing latency and improving query performance for enterprise-wide data analytics• Innovated real-time data processing strategies using Azure Event Hubs and Stream Analytics, establishing reliable data streaming workflows for seamless, high-velocity data ingestion, transformation, and analytics in the cloud, supporting immediate business decision-making• Collaborated with cross-functional teams to define data requirements and design optimized data architectures using Azure Data Lake Gen2, ensuring robust data storage solutions that support both batch and real-time data pipelines for complex analytics and reporting needs• Enhanced the deployment of end-to-end data solutions, integrating Azure Databricks and Synapse Analytics for large-scale analytics workloads, while ensuring consistency and quality across all data processing pipelines, from ingestion to visualization• Implemented highly scalable data pipelines and cloud solutions in Azure Data Factory, Azure Synapse Analytics, and Azure Databricks, supporting large-scale enterprise data workflows and ensuring seamless data processing across distributed systems with optimized performance for real-time analytics• Implemented data integration solutions to connect disparate data sources and systems, ensuring seamless data flow to orchestrate data workflows and dependencies using Azure Data Factory