Kirtesh Patel

I’m a Sr. AWS Data Engineer with 7+ years of experience designing, developing, and optimizing large-scale data platforms across Banking, Finance, Healthcare, and Retail. I focus on secure, scalable enterprise data solutions that support both batch and real-time analytics.

In my recent role, I designed and implemented enterprise-scale AWS Data Lake architectures using Amazon S3, EMR, AWS Glue, Redshift, and Snowflake, following Medallion (Bronze–Silver–Gold) patterns. I led end-to-end migration of legacy on-prem Hadoop and traditional ETL workflows to cloud-native AWS and Snowflake, including schema redesign, data model transformation into Star/Snowflake schemas, and parallel run reconciliation frameworks with 100% data validation.

I build performance-optimized data platforms using dbt, Python, PySpark, and Spark SQL—delivering reliability, testing, and CI/CD-driven maintainability. I’ve reduced transformation runtime by 30–40% with dbt incremental models and improved Snowflake query performance by 35–50% using clustering keys and micro-partition pruning, while also lowering operational costs by 20–30% through auto-suspend/resume and warehouse right-sizing.

I extend these capabilities across AWS, Azure, and GCP, implementing secure governance with IAM/RBAC/KMS, and engineering near real-time ingestion with Kinesis, Lambda, and Spark Streaming for fraud detection and operational risk monitoring. I also collaborate with data science to deliver ML-ready datasets and integrate third-party REST APIs into cloud data pipelines with DataOps-friendly tooling and monitoring.