• Designed and implemented complex ETL workflows using Azure Data Factory (ADF) to orchestrate data movement and transformation across multiple data sources and destinations.• Implemented time based and event-based scheduling for pipelines and monitoring the pipelines to avoid failures for business continuity.• Developed scalable data processing solutions with Azure Databricks and PySpark, optimizing data transformation and analytics processes to handle large volumes of structured and unstructured data.• Use Python libraries like pandas and NumPy alongside PySpark for advanced analytics and data manipulation within ADB.• Deployed Python UDFs (User-Defined Functions) in PySpark for custom data processing logic and transformations.• Utilize ADB Jobs or ADB Notebooks to orchestrate Python-based data transformation workflows and schedule recurring tasks.• Conducted performance tuning and optimization of Spark jobs in Azure Databricks, including partitioning strategies, caching mechanisms, and cluster configuration adjustments to maximize resource utilization and processing speed.• Architected and managed data warehousing solutions utilizing Azure Synapse Analytics, providing robust data storage and enabling high-performance query capabilities for business intelligence.

• Extracted, transformed, and loaded (ETL) data from various source systems to Azure Data Storage services using Azure Data Factory, T-SQL, Spark SQL, and Azure Data Lake Analytics.• Performed data ingestion into Azure Data Lake, Azure Storage, Azure SQL, and Azure Synapse Analytics, facilitating seamless data flow and storage.• Created complex data pipelines in Azure Data Factory using Linked Services, Datasets, and Pipelines to extract, transform, and load data from sources like Azure SQL, Blob Storage, and Azure Synapse Analytics.• Developed SQL Activities within Azure Data Factory to process and transform data, ensuring data readiness for analytical purposes.• Authored JSON scripts for deploying data pipelines in Azure Data Factory, automating data processing and management tasks.• Implemented data migration strategies, moving data from on-premises servers to Azure Data Lake Gen2 Storage, ensuring data integrity and continuity.• Designed, created, and queried data warehouse schemas and tables, facilitating structured data storage and efficient querying• Set up and managed storage accounts in Azure, providing end-to-end environments for running data processing jobs.• Processed data in Azure Databricks, implementing data transformation and analysis to support business intelligence and analytics requirements.• Performed ETL operations in Azure Databricks, leveraging JDBC connectors to connect to various relational database sources for data extraction and loading.• Optimized ETL processes by utilizing Azure Databricks and Spark SQL, significantly reducing data processing times and improving efficiency.

• As a Microsoft Partner, we offer solutions to our customers across the globe.• By following the ITIL process and providing the service to the customers based on SLA.• Provide engineering design across different workloads including incident & problem management, change management, security and compliance. • Service Request Management (User Provisioning, Environment requests, Deployments.• Creating Bugs for build failures and resolving the bugs by doing active collaboration with development team.• Conducted regular performance monitoring and optimization of build and release pipelines, identifying bottlenecks and implementing improvements to enhance efficiency and reliability.• We are responsible for the application to be up and running for the customers (24/7) in all regions like EMEA, NOAM, and APAC.• Responsible for Microsoft Identity and Active directory servers end to end maintenance like Capacity maintenance, Patching, make sure high availability, Monitoring and resolve issues as per SLA.• Proficient in managing patching activities for both production and non-production servers using Azure DevOps, ensuring seamless deployment and minimal downtime.• Led a large-scale project to optimize server performance by implementing automated defragmentation and rebuilding processes for over a thousand servers using Azure DevOps, resulting in significant improvements in system stability and efficiency.• Proficient in seamlessly migrating on-premises servers to cloud environments, ensuring smooth transitions and optimized performance while minimizing downtime and preserving data integrity• Implemented monitoring and alerting solutions using Azure Monitor, Application Insights, and Log Analytics, ensuring high availability and performance of applications.