Runtime improvements of 20-35% across critical workloads
Failed production runs reduced by over 40%
Daily compute consumption reduced ~20% through incremental processing
Trained 5-15 engineers on Lakehouse patterns and Spark best practices
Challenge
A consulting client had adopted Azure Databricks but faced inconsistent job performance, frequent pipeline failures, and no proper governance. Teams worked in silos with duplicate data and unpredictable costs.
Solution
I implemented production-grade Medallion Lakehouse architectures on Azure Databricks using Delta Lake and PySpark. Optimized cluster configurations, established data access controls, and created Git-based CI/CD workflows.
My Role
Data Engineer & Consultant – led architecture implementation, performance optimization, and conducted Spark workshops for client teams.
Key Deliverables
- 01Medallion Lakehouse architecture with Delta Lake
- 02Optimized Databricks cluster configurations and autoscaling policies
- 03Databricks Jobs with retry logic and dependency management
- 04Table ACLs and data masking for enterprise data access control
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