Ajith Guttikonda

At 7-Eleven, I designed and deployed an end-to-end Azure Data Lake integrated with Snowflake to centralize sales, supply chain, and customer data. I built real-time ingestion pipelines using Event Hubs, Kafka, and Databricks that processed over two million daily transactions and implemented Retrieval-Augmented Generation (RAG) workflows with Azure OpenAI and LangChain to summarize customer feedback and generate actionable sentiment insights. I also automated metadata tagging and documentation for 50K+ datasets using LLMs, improving discoverability and compliance across the data ecosystem.

During my time at U.S. Bank, I led the migration of on-prem data warehouses to Azure Data Lake and Snowflake, consolidating 15+ enterprise banking data sources. I built batch and streaming pipelines using Azure Data Factory, Event Hubs, and PySpark to process over 10TB of data daily and implemented a GenAI-powered data quality assistant with Azure OpenAI and LangChain for anomaly detection and intelligent query generation. I also delivered a Snowflake-based Customer 360 platform that enabled personalized banking insights, fraud detection, and regulatory reporting while automating CI/CD deployments using Terraform and Azure DevOps.

Data Engineer specializing in building secure, HIPAA-compliant healthcare data platforms on AWS. Experienced in centralizing patient records, appointment data, and digital health telemetry into S3-based data lakes, enabling real-time insights for doctors and administrators. Skilled in designing ETL/ELT pipelines with AWS Glue, Lambda, and Python, building streaming solutions with Kinesis, modeling datasets in Redshift and Snowflake, and delivering self-service dashboards in Power BI/QuickSight. Automated CI/CD and infrastructure with Terraform and CodePipeline, improving efficiency and data freshness from 24 hrs to under 1 hr. Tech Stack: AWS S3, Glue, Lambda, Kinesis, Redshift, Snowflake, API Gateway, RDS, Terraform, CodePipeline, Python, SQL, Power BI, QuickSight