Pavankumar Tattukolla

Designed and developed end-to-end ETL pipelines using Azure Data Factory (ADF), including pipelines, data flows, datasets, linked services, and triggers for scalable data ingestion and transformation.

Built and configured reusable ADF components such as parameterized pipelines, variables, and dynamic datasets, improving pipeline flexibility and reusability.

Implemented data ingestion frameworks using Copy Activity to load data into staging layers (ADLS/SQL), followed by transformation using stored procedures and data flows to populate target systems.

Developed and optimized data migration workflows, ensuring data accuracy, consistency, and efficient movement from source to target systems.

Monitored and maintained ADF pipelines using built-in monitoring tools, implementing robust error handling, retry mechanisms, and alerting to ensure high pipeline reliability.

Configured Integration Runtime, Linked Services, and Data Sets to securely connect and process data across multiple sources and destinations.

Automated and scheduled ADF pipeline execution using TWS (Tivoli Workload Scheduler), enabling seamless orchestration of batch data processing workflows.

Implemented Slowly Changing Dimension (SCD Type 2) techniques in data warehousing solutions to maintain historical data tracking and auditability.

• Streamlined batch data pipeline orchestration and monitoring using IBM Tivoli Workload Scheduler (TWS), ensuring reliable and timely execution of data migration and ETL workflows.
• Reduced job failures and improved SLA adherence by implementing proactive monitoring, alerting mechanisms, and standardized scheduling practices for large-scale data pipelines.
• Performed root cause analysis (RCA) for monthly job failures (abends) in collaboration with cross-functional teams, implementing permanent fixes and configuration optimizations to improve pipeline stability.
• Supported data migration processes by ensuring seamless execution of ADF pipelines and scheduled workflows, minimizing downtime and ensuring data accuracy.
• Developed and maintained operational runbooks, troubleshooting guides, and best practices documentation for TWS job scheduling and Azure Data Factory pipelines.
• Conducted knowledge-sharing sessions and technical training on workflow orchestration, scheduling strategies, and incident management, improving team productivity and operational efficiency.