Regulatory Reporting AVP - Data Governance

Do you want your voice heard and your actions to count?

Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), one of the world's leading financial groups. Across the globe, we're 150,000 colleagues, striving to make a difference for every client, organization, and community we serve. We stand for our values, building long-term relationships, serving society, and fostering shared and sustainable growth for a better world.

With a vision to be the world's most trusted financial group, it's part of our culture to put people first, listen to new and diverse ideas and collaborate toward greater innovation, speed and agility. This means investing in talent, technologies, and tools that empower you to own your career.

Join MUFG, where being inspired is expected and making a meaningful impact is rewarded.

The selected colleague will work at an MUFG office or client sites four days per week and work remotely one day. A member of our recruitment team will provide more details.

The Regulatory Reporting AVP, Data Governance will work in the Enterprise Reporting Analytics (ERA) team in Chief Data Office (CD) to support the ERA Data Officer in executing the ongoing management of the firm's enterprise data governance framework, with a particular focus on regulatory and risk‑critical data. This includes contributing to data quality, lineage, metadata, and control initiatives, supporting regulatory expectations related to data governance, accountability, and risk data aggregation.

Major Responsibilities:

Data Governance & Stewardship

* Support the implementation and ongoing execution of the enterprise data governance framework, with a focus on regulatory and risk‑critical data domains.

* Partner with Enterprise Data Owners, Data Officers, and Data Stewards to establish and maintain clear data ownership, stewardship roles, and accountability structures for Critical Data Elements (CDEs).

* Contribute to the development, documentation, and maintenance of authoritative data definitions, business glossaries, data standards, and metadata in approved governance tools (e.g., Collibra).

* Promote consistent adoption of data governance standards across regulatory reporting, risk, and finance use cases, ensuring alignment with enterprise policy and regulatory expectations.

Data Quality & Controls

* Support the design, implementation, and monitoring of data quality rules, thresholds, and key performance indicators (e.g., accuracy, completeness, timeliness, consistency) for regulatory and risk‑critical data.

* Assist in embedding data quality controls within upstream systems, data pipelines, and reporting processes, in coordination with Technology and business stakeholders.

* Monitor data quality performance and trends, support root‑cause analysis of data quality issues, and assist in defining remediation actions and sustainable control enhancements.

* Contribute to data quality reporting and management information provided to senior management, data governance forums, and regulatory stakeholders as needed.

Data Lineage & Regulatory Readiness

* Support the documentation and maintenance of end‑to‑end data lineage for regulatory reports, risk calculations, and management reporting, from source systems through transformations to final consumption.

* Ensure lineage artifacts clearly demonstrate data flows, controls, and dependencies in a manner that meets internal governance standards and external regulatory expectations (e.g., BCBS 239).

* Maintain lineage, metadata, and related governance artifacts within approved enterprise tools, ensuring accuracy, completeness, and ongoing currency.

* Prepare and coordinate documentation, evidence, and responses to support internal audits, model validations, data quality reviews, and regulatory examinations.

Change & Issue Management

* Support data governance impact assessments related to regulatory changes, new reporting requirements, system implementations, platform migrations, and changes to data sourcing or calculation logic.

* Assist in identifying, documenting, and escalating data‑related risks and issues, including data quality breaks, lineage gaps, control deficiencies, audit…