Pavan Kumar Kodati is a Lead Data Engineer / Senior Data Engineer with 12+ years of professional experience in delivering enterprise-grade application solutions across Healthcare, Pharmaceuticals, Entertainment, Financial Services, Geotechnical, and Marketing & Sales industries. He has worked across all phases of the development lifecycle, with strong expertise in data engineering, cloud platforms, big data processing, AI-driven architectures, ETL/ELT pipelines, and enterprise data warehousing.
Pavan’s recent experience includes working with Citi Bank, where he designed and built AI agentic architectures using LLMs, Google Vertex AI, Claude, PyTorch, and FastAPI for Critical Data Element discovery, metadata mapping, lineage tracing, and data governance workflows. He has also developed API-driven metadata ingestion frameworks, optimized PostgreSQL schemas, and integrated scalable AI inference pipelines for enterprise applications.
Previously, at NASDAQ, Pavan led the migration of Ruby-based data pipelines to Python 3 on AWS, building scalable and secure data platforms using Lambda, S3, EMR, Glue, EC2, SNS, and IAM. He has engineered petabyte-scale data ingestion pipelines using Apache Spark, PySpark, Scala, Databricks, and Kafka, supporting both batch and real-time data processing workloads.
His experience also includes building and optimizing data solutions for Novo Nordisk, USAA, Blue Cross Blue Shield, Florida Blue, Haley and Aldrich, Sree Venkateshwara Power Controls, and Cognizant Technology Solutions. Across these roles, he has worked extensively with AWS, Azure, Hadoop, Hive, Spark, Snowflake, Redshift, Databricks, Airflow, Terraform, Docker, Kubernetes, Python, SQL, Scala, Tableau, and Power BI.
Pavan holds an MSIT and an MBA from the University of Massachusetts Boston, along with a BTECH from JNTU. His background combines strong technical expertise, cloud data engineering, AI/ML pipeline development, data governance, DevOps automation, and business-focused analytics, making him a highly experienced professional in enterprise data modernization and cloud-based analytics platforms.