Trust & Safety Team

• Implemented a data anomaly detection framework on 12+ Redshift tables using SQL and AWS SES to automatically detect and email stakeholders about data anomalies

• Reduced 90% of ad-hoc queries by enabling automated data pipelines with API calls utilizing Python and DynamoDB

• Built an AWS Quicksight dashboard for 12+ Redshift tables to display data anomalies for stakeholders and business partners

• Maintained 90% of end-to-end code coverage by writing unit, integration, and smoke tests using Pytest

• Constructed data pipeline for automation of 1+ billion datasets to inform stakeholders and partnering teams of products traffic

• Collaborated with cross-functional team partners to create a data-driven dashboard with SQL optimizations

• Produced end-to-end data validations with Presto and Python to advance the intake of large datasets