PAVAN KRISHNA VUNNAM

Data Engineer with 3+ years of professional experience designing, orchestrating, and optimizing end-to-end data pipelines across AWS and Azure ecosystems. Expert in building serverless, scalable data architectures using AWS Glue, Lambda, Step Functions, and Azure Data Factory for batch and real-time data workflows. Strong expertise in ETL automation, orchestration, and data modeling using Databricks (PySpark), Redshift, Snowflake, and Synapse Analytics. Skilled in AI-powered knowledge retrieval integrating Amazon Kendra, Bedrock, and OpenSearch for semantic document search and automated Q&A. Proficient in Python (Pandas, boto3, PySpark) and SQL for developing reusable data pipelines, API integrations, and workflow automation. Hands-on experience in metadata tagging, compliance enforcement (ABAC), and dynamic IAM policies for ITAR/EAR governance. Adept at data migration, transformation, and integration across multiple cloud environments using Glue Jobs, Step Functions, and Azure Data Factory pipelines. Designed and delivered data visualization solutions using Power BI, Tableau, and QuickSight, driving actionable business insights. Implemented CI/CD pipelines and infrastructure-as-code automation using Terraform, GitHub Actions, and AWS CloudFormation. Collaborated with data scientists and AI engineers to embed ML models into production ETL pipelines using SageMaker and Databricks. Experienced in data security, lineage tracking, and pipeline observability using CloudWatch, Athena, and tagging frameworks. Passionate about building intelligent, automated, and secure data ecosystems that bridge analytics, AI, and business decision-making.

Positive, analytical problem-solver with strong foundation in data systems and processes. Possesses solid understanding of data modeling and database design, coupled with skills in SQL and Python. Capable of driving data-driven decision-making and improving data infrastructure.