At Amazon, I led a company-wide review of existing Data Quality (DQ) systems, identifying systemic ingestion gaps, compliance challenges, and process inefficiencies. I worked with data engineers to design technical solutions and detailed implementation roadmaps that improved analytics accuracy and reporting reliability for high-priority datasets. Partnering with the privacy team, I interpreted and operationalized new GDPR mandates, identifying all datasets impacted by European data-sharing restrictions and implementing metadata tagging to ensure compliance. I ran targeted remediation campaigns that significantly reduced ingestion delays, increased governance adoption, and eliminated critical DQ issues. The program improved reporting accuracy across multiple high-visibility programs, accelerated time-to-resolution for DQ defects, and established a sustainable compliance process covering 100% of GDPR-impacted datasets.
Position Overview
Key Responsibilities
• Conducted in-depth assessment of existing data quality (DQ) systems to identify gaps and inefficiencies.
• Led root cause analysis to uncover key ingestion, governance, and compliance challenges.
• Collaborated with data engineers to design solutions and detailed implementation plans for DQ improvements.
• Partnered with privacy teams to interpret new GDPR regulations, identifying datasets impacted by European data-sharing restrictions.
• Directed tagging and classification of sensitive datasets to ensure compliance.
Major Achievements
✓ Delivered a targeted remediation plan that improved data quality and reporting accuracy.
✓ Created a GDPR compliance tagging process for datasets subject to European sharing restrictions.
✓ Developed actionable solution roadmaps for DQ improvement, accelerating adoption across teams.
✓ Established collaborative workflows between engineering, privacy, and product teams to ensure smooth implementation.
Impact & Results
Identified and remediated high-priority DQ issues, improving analytics accuracy.
Increased compliance coverage for GDPR-affected datasets.
Accelerated resolution of ingestion errors through targeted engineering fixes.
Key Projects Delivered
🎯 DQ System Review & Gap Analysis: Evaluated current state, identified root causes, and documented improvement areas.
🎯 Solution & Implementation Planning: Designed technical and process solutions with engineering teams.
🎯 GDPR Impact Analysis & Tagging: Partnered with privacy to identify and tag data restricted from EU sharing.
🎯 Remediation Campaigns: Led cross-team initiatives to address ingestion and governance issues.
Challenges Overcome
Lack of visibility into systemic DQ issues → implemented structured review and gap analysis.
Fragmented remediation approach → developed coordinated solution roadmaps with engineering.
GDPR compliance risk for EU data → identified and tagged restricted datasets.
Limited alignment between privacy and engineering → established joint workflows for governance enforcement.
Skills Developed
• Root cause analysis
• System review & evaluation
• Solution design & implementation planning
• GDPR compliance alignment
• Data classification & tagging
• Cross-functional collaboration
• Stakeholder communication
• Governance strategy