Key Responsibilities
Data Quality Strategy & Framework
Design, implement, and operationalize the enterprise Data Quality Management framework, standards, procedures, and controls.
Translate DQ standards into actionable controls and practices across business domains and enterprise data platforms.
Align Data Quality practices with enterprise Data Governance, Risk Management, and Data Architecture strategies.
Establish scalable processes that improve overall Data Quality maturity across the organization.

Data Quality Monitoring, Controls & Issue Management
Design and implement automated DQ rules, validation checks, profiling, reconciliation, and certification processes for Critical Data Elements (CDEs).
Establish continuous monitoring and proactive issue detection across enterprise data pipelines.
Implement issue management workflows with clear ownership, SLA tracking, escalation, root-cause analysis, and remediation.
Identify recurring data quality trends and escalate material risks through appropriate governance channels.
Ensure data pipelines and implementations meet required data quality and control standards prior to production release.

Stakeholder & Stewardship Leadership
Partner with Data Owners and Data Stewards to establish clear accountability for data quality.
Facilitate domain-level DQ forums and working groups and provide guidance on DQ best practices and control design.
Collaborate with Data Governance, Metadata Management, Architecture, and Engineering teams to align DQ rules with lineage, classification, and enterprise data standards.
Support business testing, UAT, and pre- and post-production validation to ensure data integrity throughout delivery lifecycles.

Reporting, Automation & Continuous Improvement
Develop enterprise DQ dashboards, KPIs, scorecards, and reporting for senior leadership.
Track remediation effectiveness, trends, risk exposure, and continuous improvement metrics.
Identify opportunities to automate DQ monitoring, profiling, anomaly detection, root-cause analysis, and remediation.
Evaluate emerging technologies including Agentic AI, LLMs, autonomous agents, and AI-assisted development tools to improve Data Quality capabilities and operational efficiency.

Required Qualifications
Bachelor's degree in Information Systems, Data Management, Computer Science, or a related field.
8+ years of experience in Data Quality, Data Governance, or Enterprise Data Management, including 3+ years leading enterprise-wide DQ initiatives.
Demonstrated experience designing, implementing, and operationalizing enterprise Data Quality frameworks, controls, and programs.
Experience with enterprise DQ platforms such as Informatica Data Quality, Collibra DQ, Talend, Ataccama, or equivalent.
Strong SQL skills and experience with data profiling, reconciliation, data analysis, DQ rule design, and DQ metrics.
Experience embedding Data Quality controls into ETL/ELT, MDM, cloud, data lake, and modern enterprise data platforms.
Strong understanding of metadata management, data lineage, data classification, Critical Data Elements, and lifecycle management.
Demonstrated experience using modern AI tools to accelerate Data Quality engineering, rule creation, profiling, issue analysis, remediation, and documentation.
Experience with AI-assisted development tools such as GitHub Copilot, Claude Code, OpenAI Codex, Cursor, VS Code, or similar tools.
Experience applying AI agents to automated DQ rule generation, anomaly detection, issue analysis, or remediation.
Strong stakeholder management skills with the ability to work across business, governance, architecture, engineering, risk, and leadership teams.

Preferred Qualifications
Experience with Azure and Databricks or comparable cloud/lakehouse technologies.
Knowledge of BCBS 239, GDPR, SOX, ISO 8000, or similar regulatory and controls frameworks.
Experience leading AI adoption initiatives across Data Governance, Data Management, Analytics, or Engineering functions.
Experience building or using AI agents for metadata enrichment, automated lineage extraction, or intelligent Data Quality automation.
Relevant Data Quality, Data Management, cloud, Informatica, or Databricks certifications.

Data Quality Manager

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