Data Quality Analyst, Vice President

Overview

In this role you safeguard data quality across Citi’s supervisory and transaction monitoring controls, enabling reliable risk decisions. You will manage the data quality lifecycle, identify critical data elements, and work with cross-functional teams to monitor, investigate, and remediate data issues. You’ll translate governance requirements into practical rules and KPIs, helping strengthen Citi’s control environment. This position offers exposure to global markets, data governance, and risk management at a leading financial institution, with a clear impact on control effectiveness.

Pay / Benefits

• hybrid work model
• learning and development resources
• wellbeing and financial benefits
• collaboration across business, technology, and risk
• global exposure
• long-term career development

Responsibilities

• Identify critical data elements and define data quality rules through structured SQL reviews.
• Monitor data quality exceptions, perform root cause analysis, and coordinate remediation with technology teams.
• Prioritize data-related risks and issues, escalating to ensure timely resolution and control effectiveness.
• Conduct ad-hoc data analysis to reconcile and validate datasets for stakeholder review.
• Consult with business users to assess integration needs and opportunities for automation.
• Develop mitigation strategies to address data gaps and reduce control risk.
• Contribute to KPI and metric development to track data quality and control performance.
• Ensure data activities align with Citi risk frameworks, policies, and risk appetite.

Key requirements

• Proficiency in SQL for complex data interrogation and structured reviews.
• Experience in data quality management, rules definition, and threshold monitoring in regulated environments.
• Ability to perform root cause analysis and translate findings into remediation actions.
• Understanding of risk and control frameworks and governance standards.
• Strong analytical skills with experience reconciling data from multiple sources.
• Clear, confident communication to diverse internal stakeholders.

• analytical thinking
• stakeholder communication
• collaboration
• SQL
• data quality management
• threshold monitoring

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