Manager, Data Quality Product Owner

Purpose

The Data Management and Quality team is responsible for enterprise Data Quality Governance and Issue Management. The team establishes common standards, controls, processes, measures, and enablement to help business lines consistently manage data quality across the data lifecycle. The Manager, Data Quality Product Owner provides centralized data quality leadership while also supporting the strategy, roadmap, and delivery of enabling data quality capabilities.

The role partners with business, data, risk, compliance, architecture, and technology teams to operationalize enterprise data quality requirements, provide subject-matter guidance, oversee issue management and reporting, and drive consistent adoption across the organization. The role also translates stakeholder needs into prioritized enhancements for data quality tools, workflows, dashboards, and related data management capabilities.

Accountability

• Act as a centralized Data Quality lead and Centre-of-Excellence partner for business lines, data domains, and platform teams.
• Provide oversight, challenge, and subject-matter guidance to help, data stewards, and delivery teams meet their data quality accountabilities.
• Contribute to the product vision, roadmap, and backlog for enterprise data quality tools, workflows, dashboards, integrations, and automation.
• Coordinate dependencies, risks, decisions, and delivery milestones, and communicate progress and escalations to governance forums and senior stakeholders.
• Design, develop, test, and implement Data Quality rules, controls, validations, and monitoring solutions to ensure data accuracy, completeness, consistency, timeliness, and integrity.
• Perform data profiling, data analysis, anomalies, and control gaps across critical datasets.
• Configure and maintain Data Quality frameworks, thresholds, exception handling, and automated monitoring processes to proactively detect and manage data quality issues.
• Collaborate with business stakeholders, data stewards, analysts, and technology teams to translate business requirements into technical Data Quality rules and validation logic.
• Develop and optimize SQL queries, scripts, and data validation processes to support rule execution, reconciliation
• Analyze Data Quality metrics, dashboards, scorecards, and control results to identify trends, risks, and opportunities for improvement. 
• Partner with engineering, architecture, analytics, and platform teams through design, delivery, testing, release, and adoption of data quality capabilities.
• Partner with engineering and platform teams to integrate Data Quality controls into data pipelines, ETL/ELT processes, cloud platforms, and enterprise data solutions.
• Develop technical specifications, rule logic documentation, testing artifacts, and reusable rule templates to promote consistent implementation across domains.
• Support audit, regulatory, risk, and compliance requirements by providing Data Quality evidence, rule documentation, control outputs, and remediation status.
• Contribute to the continuous improvement of Data Quality standards, frameworks, automation capabilities, and monitoring processes.
• Ensure alignment of Data Quality controls with enterprise data governance practices, including metadata, lineage, critical data elements, reference data, privacy, and data protection requirements.
• Participate in Agile delivery processes, including requirements analysis, user story refinement, sprint planning, testing, deployment, and production support.
• Identify opportunities to automate manual data validation activities and improve overall Data Quality control effectiveness through scalable technical solutions.

Experience

• 4+ years of hands-on experience in Data Quality development, data engineering, and enterprise data management on cloud platforms (GCP, Azure, AWS).
• Hands-on experience designing, developing, testing, and implementing Data Quality rules, controls, validations, monitoring frameworks, and automated data quality checks.
• Proficiency in ELT/ETL tools such as DBT, SODA, DataStage, and Talend for data transformation, validation, reconciliation, and pipeline monitoring.
• Advanced SQL expertise with the ability to develop complex queries, perform data profiling, root-cause analysis, data reconciliation, and issue investigation. SQL proficiency is mandatory.
• Experience implementing Data Quality monitoring on cloud-based data platforms and modern data architectures.
• Hands-on experience in data profiling, anomaly detection, threshold management, exception handling, and issue remediation processes.
• Experience building and maintaining Data Quality dashboards and reporting solutions using Power BI to monitor KPIs, trends, controls, and data quality outcomes.
• Experience developing reusable Data Quality rule libraries, standards, and control frameworks for enterprise-wide adoption.
• Strong analytical and problem-solving skills with the ability to translate business requirements into technical Data Quality rules and scalable monitoring solutions.
• Experience working in Agile environments, including requirements gathering, user story development, backlog refinement, sprint delivery, and testing.
• Knowledge of metadata, data lineage, critical data elements, reference data, and enterprise data governance practices to support Data Quality implementation.
• Experience supporting audit, regulatory, risk, and control initiatives by providing data quality evidence, issue analysis, remediation tracking, and control reporting.
• Ability to collaborate effectively with Data Engineers, Architects, Data Stewards, Business Analysts, and business stakeholders to deliver enterprise-scale Data Quality solutions.
• Strong communication and stakeholder management skills, with the ability to influence teams and drive adoption of Data Quality standards and best practices.

Qualifications

• Bachelor’s degree in computer science, Information Technology, Data Management, Engineering, or related discipline.
• 4+ years of hands-on experience in Data Quality implementation, Data Governance, Enterprise Data Management, or Data Engineering.
• Strong working knowledge of data quality frameworks, controls, governance, issue management, and operating-model practices.
• Working knowledge of Agile delivery, requirements definition, backlog management, and iterative capability development.
• Ability to interpret data, dashboards, operational metrics, and control results; Strong knowledge of SQL is compulsory.
• Strong experience designing, developing, testing, and implementing Data Quality rules, validations, controls, and monitoring solutions across large-scale data platforms.