I've built scalable data pipelines and analysis-ready datasets at Amazon, migrating legacy ETL workflows to Spark-based pipelines and expanding operational reporting through new ingestion workflows and tables.
During my Amazon internship, I improved an operational anomaly-detection framework, reducing false positives by 30%. I also partnered with Business Intelligence Engineers to improve Amazon QuickSight dashboards and automate manual data-processing workflows.
Previously, at The Hackett Group, I developed SQL and Amazon Redshift data models, ETL workflows, dashboards, and reporting solutions for multiple enterprise clients. I translated complex business requirements into reliable data solutions, optimized reporting performance, and mentored junior analysts.
I'm experienced in AWS, Snowflake, dbt, Airflow, Databricks, Spark, and CI/CD, with hands-on work in real-time and CDC pipelines, dimensional modeling, data quality testing, and distributed analytics workflows.
