Manager - Data Quality

The Enterprise Technology Services organization partners with every part of the American Express business to power the company’s growth and innovation with trust and efficiency, and drive competitive differentiation with speed. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally. Specifically, our team is responsible for the company’s technology engineering, architecture, and infrastructure, providing 24x7 support to ensure an uninterrupted, high-quality experience for customers and colleagues. We also provide product management for core enterprise platforms, and lead technology risk and information security, enterprise data governance and platforms, digital product and design, and enterprise AI platforms on behalf of the company.

This role is part of the newly formed Enterprise Technology Services (ETS) Data Office. The Manager, Data Quality (DQ) will lead the strategy and execution of enterprise Data Quality capabilities across ETS. This role is responsible for modernizing how data quality risk is measured, monitored, and remediated while establishing scalable controls that improve trust in data used for operations, analytics, AI, regulatory processes, and decision-making. This role will build a proactive, AI-enabled Data Quality practice focused on prevention over detection, automation over manual effort, and measurable business outcomes over compliance-driven activities.

• Execute the enterprise Data Quality strategy across ETS aligned to industry-leading data management and risk frameworks.

• Standardize Data Quality measurement and monitoring across critical data domains, including data at rest, in motion, and at point of entry.

• Operationalize scalable Data Quality controls and governance practices that reduce operational, regulatory, and customer risk.

• Lead enterprise root-cause analysis and remediation efforts to reduce recurring data issues and improve control effectiveness.

• Build AI-enabled capabilities for anomaly detection, intelligent monitoring, automated profiling, and predictive Data Quality insights.

• Modernize Data Quality reporting through automated dashboards, drill-down analytics, and executive-ready risk transparency.