At UnitedHealth Group, I investigate specific stop loss claims with SQL, uncovering $1.2 million in potential recovery through provider mapping error analysis.
I build Python validation routines, automate Snowflake cleansing, and manage Azure Data Factory pipelines to improve claim accuracy, reconciliation, and processing speed. My Power BI dashboards reduced reporting latency by 50% by making claim metrics and denial trends accessible to leadership.
Previously at Capgemini, I improved claimant identification accuracy by 30%, reduced processing latency by 40%, and reduced data errors by 95% through SQL, ETL, Python, Informatica, Talend, and Power BI work.
