Mahesh Reddy Komatla

Built LLM-enabled data quality workflows for a fintech environment, improving data accuracy and decision support. Designed and optimized orchestration for data pipelines using Dagster and Prefect to increase pipeline efficiency and enable near real-time analytics. Developed Azure Data Factory and Azure Databricks solutions with SQL Server sources and PySpark/Delta Lake transformations, improving governed ingestion and analytics readiness. Set up CI/CD automation with Git and Terraform to support repeatable releases and reduce deployment risk across agile teams.

Modernized version control and observability practices to improve platform availability and proactive incident handling. Implemented AI-native, model-driven features that improved engagement and streamlined deployment across multiple microservices. Standardized AWS Glue-based ETL patterns into reliable ingestion for reporting using Redshift, while improving event-driven processing through Kafka integration. Automated batch ingestion workflows with AWS Lambda and strengthened compliant access controls for trusted analytics in healthcare.

Contributed to engineering decisions that improved system performance and supported scalable, maintainable data infrastructure. Built and analyzed analytics assets using SQL with BigQuery and Looker-style reporting to improve metric consistency and insight delivery. Designed and monitored data transformation pipelines using GCP orchestration and storage-based ingestion patterns. Improved code reliability with testing strategies and supported data processing workflows using Python-based ingestion and validation.