Job Description The Data Quality Management Lead is responsible for designing, implementing, and operationalizing the enterprise Data Quality (DQ) framework across business domains. This role ensures that critical data assets are accurate, complete, consistent, timely, and fit-for-purpose to support regulatory reporting, analytics, operational excellence, and strategic decision-making.

Key Responsibilities Data Quality Strategy, Framework, and Implementation Develop, implement, operationalize, and support the ongoing execution of the enterprise Data Quality Management framework. Establish data quality standards, procedures, and control mechanisms, translating them into actionable controls and practices. Ensure alignment with Enterprise Data Governance and Risk Management frameworks and integration with Enterprise Data Architecture. Data Quality Monitoring, Controls, and Issue Management Design and implement automated data quality rules and validation checks across Critical Data Elements (CDEs). Integrate automated and AI-driven monitoring capabilities for continuous surveillance, real-time issue identification, audit trails, reporting, and remediation workflows. Implement issue management processes with clear ownership, SLA tracking, escalation paths, and resolution management. Monitor recurring data quality trends and escalate material risks through governance channels. Stakeholder Engagement and Data Steward Enablement Partner with Data Owners and Data Stewards to embed accountability for data quality across the organization. Facilitate domain-level Data Quality forums and governance working groups. Provide subject matter expertise and guidance on data quality best practices, controls, and framework adoption. Enterprise Data Quality Standards and Controls Establish standards for data profiling, data requirements, validation, reconciliation, and testing activities across enterprise initiatives. Ensure initiatives include sufficient business testing and UAT activities to certify data quality before implementation. Support the identification and resolution of data-related issues throughout project lifecycles, including pre- and post-production phases. Metadata and Data Controls Integration Collaborate with Data Governance, Metadata Management, and Data Architecture teams. Ensure Data Quality rules align with data lineage, metadata, and data classification frameworks. Support the integration of data quality capabilities within enterprise data platforms, including ETL, MDM, data lakes, and cloud environments. Ensure data quality controls are embedded within enterprise data pipelines and certification processes. Innovation and Continuous Improvement Measure and report on data quality performance, metrics, and maturity across the enterprise. Benchmark data quality capabilities against industry leading practices. Evaluate emerging technologies and AI-based solutions, including Generative AI, LLMs, autonomous agents, and advanced automation tools to enhance Data Quality maturity and effectiveness.

Required Qualifications Bachelor’s degree in Information Systems, Computer Science, Data Management, or a related field. 8+ years of experience in Data Governance, Data Quality, Data Management, or related disciplines. 3+ years leading enterprise-wide Data Quality initiatives and programs. Proven experience implementing Data Quality programs, Data Governance frameworks, Data Lifecycle Management, and controls frameworks. Hands-on experience with Data Quality platforms such as Informatica Data Quality (IDQ), Collibra DQ, Talend, Ataccama, or equivalent tools. Strong knowledge of data profiling, data analysis, Data Quality rule design, and Data Quality metrics. Demonstrated expertise leveraging modern AI technologies to support data quality engineering, profiling, analysis, metadata alignment, issue management, root cause analysis, remediation, and documentation. Experience with agentic coding tools such as GitHub Copilot, Claude Code, OpenAI Codex, Cursor, VS Code AI extensions, or equivalent technologies. Experience leveraging AI agents for automated Data Quality rule generation, anomaly detection, and issue remediation. Strong understanding of integrating AI-assisted controls within ETL, MDM, data integration, and cloud platforms.

Nice-to-Have Qualifications Experience leading AI adoption initiatives across Data Governance, Data Management, Analytics, or Engineering teams. Knowledge of BCBS 239, GDPR, SOX, ISO standards, and other regulatory or control frameworks. Relevant industry certifications such as CDMP, DCAM, CBIP, ISO 8000, or equivalent. Experience with Azure, Databricks, or other hyperscale cloud platforms. Experience building AI agents for metadata enrichment, automated lineage extraction, and data discovery capabilities.

Skills & Competencies Enterprise Data Quality Management Data Governance & Data Controls Data Stewardship & Data Ownership Models Metadata Management & Data Lineage Data Profiling & Data Analysis Critical Data Elements (CDEs) Data Validation & Reconciliation ETL, MDM, and Data Platform Controls Artificial Intelligence & Agentic Automation Stakeholder Management & Executive Communication Regulatory Compliance & Risk Management Continuous Improvement & Innovation

60-80/hr