I've built cloud data engineering solutions at IBM, including a Python and Jinja framework that dynamically generates 600+ Airflow DAGs from BigQuery metadata. I also created an end-to-end observability framework for pipeline monitoring.
Across Publicis Sapient, Verizon, and Infosys, I've developed and optimized ETL pipelines using Databricks, Apache Spark, PySpark, BigQuery, Google Cloud Composer, and Delta Lake. My work includes performance tuning, cost optimization, data lake design, Teradata migration, and automated workflow orchestration.
I've also applied Python, Pandas, and Matplotlib to churn and attrition analysis projects, translating data into targeted retention recommendations. I'm certified as a Google Professional Data Engineer, Google Professional Cloud Architect, Databricks Associate Data Engineer, and AWS Machine Learning Specialty professional.
