Led a strategic review and redesign of Amazon’s enterprise data quality initiatives. Partnered with engineering, product, and operations teams to identify process gaps and workflow inefficiencies. Designed improved governance strategies, defined measurable data quality attributes and KPIs, and created a streamlined company-wide survey framework to surface issues and prioritize high-impact tables. Delivered a comprehensive roadmap to strengthen data accuracy, compliance, and operational efficiency across critical systems.
Project Overview
Challenges & Solutions
Challenges
Existing data quality initiatives were not effectively addressing process gaps or accuracy issues
No standardized framework for defining and measuring data quality attributes across teams
Limited ability to prioritize critical data tables based on downstream impact
Lack of clear feedback loops between engineering, product, and operational teams
Solutions
Led a strategic review of Amazon’s enterprise data quality initiatives to identify weaknesses and missed opportunities
Collaborated with engineering, product, and operations teams to gather feedback and document pain points in current workflows
Designed improved data governance strategies and created detailed proposals for process and tool enhancements
Defined measurable data quality attributes and KPIs to evaluate and track improvements over time
Developed a company-wide campaign framework, including a streamlined 5-question engineering survey, to surface quality issues quickly and prioritize high-impact tables for remediation