Saikrishna Gajjela

• Designed and implemented data pipelines to maintain data integrity and generate key performance indicators (KPIs) reflecting data passage. Stored and manipulated configurations in MongoDB using PyMongo.
• Developed and maintained Azure Analysis Services models for business intelligence and data analytics requirements.
• Utilized Kafka functionalities for messaging systems and loaded data from REST endpoints to Kafka.
• Created JSON scripts for deploying pipelines in Azure Data Factory (ADF) and implemented UNIX shell scripts for
  database connectivity and parallel job execution.
• Worked on big data integration and analytics using Hadoop, PySpark, Kafka, Storm, and web Methods. Managed relational database services with Azure SQL and integrated various data storage solutions.
• Implemented copy activity, custom Azure Data Factory pipeline activities.
• Automated data quality checks and error handling processes to ensure the integrity and reliability of datasets.
• Integrated Azure SQL's features for high availability, automated backups, and performance optimization.
• Data ingestion to one or more Azure services (Azure Data Lake, Azure Storage, Azure SQL, Azure DW), and processing the data in Azure Databricks.
• Integrated SQLite database for data storage and retrieval, implementing CRUD operations, and ensuring data integrity.
• Utilized Java programming language and XML for backend logic and layout design respectively.
• Importing and exporting databases using SQL Server Integration Services (SSIS) and Data Transformation Services (DTS) packages.
• Managed relational database services utilizing Azure SQL, ensuring reliability, scalability, and maintenance of critical data stores.

· Developed scalable solutions on Azure to sustain backend infrastructure for mobile banking application using services such as Azure Data Factory, Azure Databricks, and Azure SQL Database.

· Created migration strategies for transitioning legacy banking systems to Azure, ensuring smooth integration with tools like Azure Lift and Shift and Azure Migrate.

· Employed Azure Blob Storage, Azure HDInsight, and Azure Data Lake Storage to securely store app data.

· Optimized application performance using Apache Spark's distributed processing capabilities on the Azure platform for large datasets.

· Constructed a system in Azure Data Lake to store, process, and analyze significant big data workloads.

· Implemented Azure Data Lake security controls, including access control lists (ACLs), and Azure Active Directory integration, to secure sensitive data assets.

· Automated execution and monitoring of data pipelines using integrated GitHub tools within Azure environments.

· Employed GitHub to manage versions of data transformation scripts, ensuring uniformity in different environments.

· Rigorously tested data pipelines to guarantee the accuracy and reliability of financial information, while seamlessly integrating with Azure services like Azure Data Lake Storage and Azure Cosmos DB.

· Created and implemented microservices-based applications on Microsoft Azure Service Fabric ensuring high availability.

· Developed microservices in Azure Service Fabric utilizing Reliable Actors and Reliable Services.

· Evaluated ETL process errors and data inconsistencies for troubleshooting.

· Implemented incremental ETL strategies to enhance efficiency.

· Developed, tested, and automated complex ETL pipelines using Azure Data Factory, ensuring efficient data ingestion, transformation, and loading from diverse data sources to Azure SQL Database, and Azure Data Lake Storage.

· Leveraged Azure Data Migration services for extensive data warehousing tasks.

· Leveraged Apache Spark on Azure Databricks for distributed computing, facilitating the processing and analysis of large banking data.

· Delivered secure and efficient storage solutions for structured and unstructured data using Azure Blob Storage, Azure Data Lake Storage, and Azure HDInsight.

· Implemented incremental ETL strategies in Azure Data Factory, reducing data processing times, and optimizing resource usage by only processing changed data.

· Developed comprehensive data models in Azure SQL Database, ensuring optimal database performance, and supporting complex queries and reporting needs.

· Deployed Azure Stream Analytics and Azure Event Hubs for real-time data processing and analytics, providing up-to-the-minute insights into business operations.

· Ensured data security and compliance with industry standards by implementing Azure Key Vault, Azure Policy, and Role-Based Access Control (RBAC) across Azure resources.

· Developed interactive dashboards and reports using Power BI, integrated with Azure services, providing stakeholders with real-time insights and actionable intelligence.

· Automated data workflows using Azure Logic Apps and Azure Functions reduce manual intervention and optimize data processing tasks for better efficiency.

· Collaborated with cross-functional teams to gather requirements and translate business needs into technical specifications for data solutions.

· Implemented data visualization tools like Tableau and Power BI to create dashboards and reports for business stakeholders.

· Managed version control and deployment of data applications using Git, Docker, and Jenkins.

· Created stored procedures for automating periodic tasks in SQL Server.