About Me
Data Engineer with 3+ years of experience building scalable data pipelines and cloud cost optimization solutions on Databricks, PySpark, Python, SQL and AWS. Delivered approximately $2M in infrastructure cost savings and…
Data Engineer with 3+ years of experience building scalable data pipelines and cloud cost optimization solutions on Databricks, PySpark, Python, SQL and AWS. Delivered approximately $2M in infrastructure cost savings and a 95% reduction in pipeline processing time. Hands-on with GenAI on Databricks, including Retrieval-Augmented Generation (RAG), semantic search and AI-powered analytics using Vector Search and Genie. Databricks Certified Data Engineer Associate.
Experience
Data Engineering, Management & Governance Associate
<p>Delivered approximately $2M in infrastructure cost savings by analyzing cluster utilization, SQL Warehouse usage and workload patterns across Databricks and AWS, and recommending right-sized clusters and warehouses.</p><p> Built a query-level cost allocation dashboard using Databricks and ThoughtSpot, enabling warehouse cost visibility through query tagging and user-level attribution.</p><p> Developed an automated DynamoDB to Delta Lake ingestion pipeline using AWS Lambda to process 10 GB+ of daily operational data via incremental timestamp-based processing, reducing execution times by 95% while maintaining strict data deduplication.</p><p> Evaluated Databricks runtime versions and Serverless vs. All-Purpose compute workloads, recommending optimal compute selection and performance improvements for cost efficiency.</p>
PROJECTS
Databricks Cost Optimization
<p>Delivered approximately $2M in infrastructure cost savings by analyzing cluster utilization, SQL Warehouse usage and workload patterns across Databricks and AWS, and recommending right-sized clusters and warehouses. • Built a query-level cost allocation dashboard using Databricks and ThoughtSpot, enabling warehouse cost visibility through query tagging and user-level attribution. • Developed an automated DynamoDB to Delta Lake ingestion pipeline using AWS Lambda to process 10 GB+ of daily operational data via incremental timestamp-based processing, reducing execution times by 95% while maintaining strict data deduplication. • Evaluated Databricks runtime versions and Serverless vs. All-Purpose compute workloads, recommending optimal compute selection and performance improvements for cost efficiency.</p>