Manager, Data Quality & Controls

The Role:

Morningstar DBRS is seeking a Manager, Data Quality & Controls to lead the development and execution of the enterprise data quality and controls program. Reporting to the Senior Director, Data Products, this role will assess existing controls, identify gaps, and establish standards, processes, and governance to ensure critical ratings, regulatory, and enterprise data is accurate, complete, well-documented, and fit for purpose.

The Manager will drive the data controls roadmap, design scalable quality controls, and partner across Data Management, Credit Operations, Technology, Legal, Risk, Compliance, and Product teams to embed strong data practices into operations, regulatory reporting, platform modernization, and AI initiatives.

The ideal candidate has experience building or enhancing data governance and control frameworks within a regulated financial services environment and can influence stakeholders across all levels of the organization.

Key Responsibilities

Data Controls & Governance

Lead the design and ongoing enhancement of the enterprise data controls framework, including governance standards, workflows, metrics, issue management, and remediation processes. Assess existing controls, identify gaps, and drive improvements that align with regulatory requirements, audit expectations, and industry best practices. Own the data controls roadmap and promote data stewardship, accountability, and ownership across the organization.

Standards, Procedures & Data Quality

Develop and maintain data governance standards, procedures, and controls related to data quality, metadata, lineage, documentation, classification, retention, and issue management. Implement data quality checks, validations, and monitoring processes to ensure critical datasets remain accurate, complete, and compliant with legal, regulatory, and internal requirements.

Data Platform & External Feed Controls

Partner with business and technology teams to support platform modernization and data-feed initiatives, ensuring data integrity and control continuity throughout migrations and enhancements. Define control requirements for data products, integrations, APIs, analytics, and AI-enabled solutions, while establishing quality standards and validating migration outcomes.

Leadership & Team Development

Lead and develop a team of data analysts by providing coaching, setting priorities, and overseeing deliverables. Drive excellence in data quality monitoring, metadata management, issue remediation, and the adoption of data governance best practices and tools.

AI-Ready Data Management

Establish data and metadata standards that support AI, analytics, and automation initiatives. Ensure datasets are properly documented, governed, and controlled, with clear ownership, lineage, and quality measures. Collaborate with Compliance, Legal, Risk, and Technology teams to promote responsible AI practices, data privacy, and transparent use of enterprise data assets.

Qualifications & Experience 

• Bachelor's degree in Business, Economics, Finance, Data Science, Computer Science, or Management Studies (Master's a plus). 

• 5 + years of Proven experience building a data controls governance program in a regulated financial services environment. 

• Ability to work independently with senior managers and align stakeholders.  

• Strong understanding of controls, governance frameworks, data quality, metadata, and lineage. 

• Experience authoring policies, standards, and procedures, data-quality controls and KPIs. 

• Experience supporting regulatory data and reporting processes; familiarity with data-related regulatory expectations in financial services is a strong asset. 

• Prior people-management or team-lead experience, with the ability to coach and develop analysts is a plus, but not required  

Technical Skills 

Technical requirements emphasize control execution, data-quality oversight, and monitoring. 

• Cloud and data platforms: exposure to modern cloud platforms: Snowflake, Azure, or AWS 

• Data controls and catalog platforms: experience with metadata, lineage, and catalog tools such as DataHub, Collibra, Alation, Informatica, Microsoft Purview, or equivalents. 

• SQL: ability to query, analyze, and validate large datasets in MS SQL Server or similar platforms. 

• Data quality and lineage: data profiling, reconciliation, root-cause analysis, and end-to-