Raghurami Reddy VEMANNAGARI

• Engineered scalable and resilient data migration pipelines utilizing Python, PySpark, AWS Glue, Lambda, Step Functions, S3, RDS PostgreSQL, CloudFormation, and integrated AWS services to orchestrate migration from legacy mainframes to Guidewire Policy Center Cloud for a leading insurance provider. • Designed advanced data transformation logic and optimized performance-critical SQL queries, embedding robust error-handling to enforce business rules and maintain data integrity across millions of records. • Orchestrated high-throughput data workflows by batching heavy loads and managing execution with Amazon SQS, significantly accelerating processing while proactively eliminating bottlenecks. • Developed a comprehensive data reconciliation and validation framework to track migrated volumes and financial amounts, ensuring transparency, regulatory compliance, and auditability. • Innovated data pipeline and storage optimizations that delivered a 30–40% reduction in overall processing time while improving scalability, reliability, and operational cost-efficiency. • Led efforts to automate deployment and infrastructure provisioning through AWS CloudFormation and GitLab CI/CD, standardizing deployment operations and mitigating manual errors. • Participated in peer code reviews, fostering a collaborative engineering culture and driving adoption of best practices, code quality, and continuous improvement in delivery.

• Designed and developed scalable pipelines to ingest transactional data from multiple source systems into Amazon S3 as Delta Lake tables, improving data reliability, query performance, and audit readiness. • Implemented Databricks Auto Loader with Delta Lake to enable reliable batch ingestion, incremental updates, schema evolution, and full historical data retention, ensuring data accuracy, traceability, and enterprise trust. • Optimized data processing and query performance by applying Delta Lake optimization techniques such as partitioning, efficient filesizing, and compaction, reducing query execution times by up to 40% and lowering compute costs. • Automated end-to-end pipeline orchestration using Databricks Jobs & Workflows, enabling dependency management, retries, and monitoring, which reduced manual intervention and improved system reliability and operational efficiency. • Collaborated with cross-functional teams to strengthen data governance, leveraging Unity Catalog to enforce fine-grained access control, data lineage, and data quality standards across the analytics lifecycle.

• Designed and implemented end-to-end data pipelines by migrating ETL workloads to AWS, significantly improving scalability, maintainability, and processing efficiency. • Utilized AWS services such as Glue, Lambda, Step Functions, and Redshift to enable secure data ingestion, orchestration, and transformation, strengthening data management and analytics capabilities. • Developed custom data classifiers for non-standard file formats, improving schema recognition and boosting data quality and usability. • Collaborated with cross-functional teams to troubleshoot issues, conduct root cause analysis, and implement preventive solutions, ensuring high system stability and resilience.

• Spearheaded the lift and shift migration of four .Net enterprise applications into AWS for a prominent banking client. • Enhanced existing on-premises applications through framework upgrades and integration of Nexus and MAD ID. • Implemented robust encryption and decryption protocols for connection strings, passwords, and licenses, ensuring data security. • Leveraged AWS services like SSM Parameter Store and API Gateway to optimize application performance and scalability.