AVP/VP, Risk Data Quality Senior, READ, Risk Management Group

Job Description

The Risk Data Quality Senior plays a pivotal role in implementing and maintaining robust data quality and data governance frameworks for risk-related data. This role is centred on the operational execution and hands-on management of data quality and data governance activities, driving practical improvements and ensuring adherence to established frameworks including BCBS239.  The successful candidate shall support Data Steward on managing Data Quality, and

(i)     facilitate alignment across cross-functional teams towards tangible data quality improvements

(ii)    ensure issues raised in Data Issues Log in SG and overseas locations are properly followed up, status reported and resolved within expected timelines

(iii)   define critical data attributes and data quality requirements

(iv)   ensure fitness of data for operational and strategic use

(v)   drive and enable cross-collaboration with RMG units on Data Governance matters

Job Duties and Responsibilities

1. Data Quality Operations & Improvement:

• Manage, assign criticality and prioritise the list of data quality issues for resolution.
• Perform detailed data profiling, analysis, and validation to identify data quality issues, root causes, and potential impacts.
• Develop, implement, and maintain data quality rules, checks, and dashboards to monitor critical data attributes.
• Oversee and directly participate in the remediation of identified data quality issues, working collaboratively with data owners, source system teams, and tech to implement fixes and prevent recurrence.

1. Data Governance Implementation & Stewardship Support:

• Operationalize and enforce established data governance policies, standards, and procedures for data quality, data ownership, metadata, and data lifecycle management.
• Support the data stewardship program by providing training, guidance, and tools to Risk Units Data Champions.
• Manage and maintain core data governance artifacts such as metadata repositories, business glossaries, and data catalogs, ensuring their accuracy and completeness.
• Monitor and report on adherence to data governance policies and standards, identifying non-compliance and supporting corrective actions.

1. Monitoring, Measurement & Reporting:

• Design, implement, and manage data quality monitoring frameworks and operational reports.
• Define, track, and report on key data quality indicators (DQIs), analyzing trends, identifying anomalies, and communicating findings to relevant stakeholders.
• Prepare regular, detailed data quality reports and presentations for management and operational teams, highlighting data quality status, issues, and progress on remediation.
• Develop and maintain documentation for data quality issues, resolutions, and process improvements.

1. Stakeholder Collaboration & Communication:

• Collaborate closely with RMG users, Tech teams, to understand data requirements, clarify data definitions, and address data quality challenges.
• Act as an expert resource and primary point of contact for day-to-day data quality and data governance operational queries and issues.
• Facilitate discussions and problem-solving sessions related to specific data quality problems.

1. Tooling & Process Optimization:

• Utilize and optimize data quality and data governance tools (e.g., data profiling tools, data cleansing tools, metadata management platforms, data cataloging solutions) in daily operations.
• Identify and recommend opportunities to improve operational efficiency through process standardization and automation within the data quality and data governance domains.
• Develop and refine operational procedures and guidelines for data quality checks, issue resolution, and data governance processes.

1. Manage and govern BCBS239:

• Manage yearly BCBS239 attestation, work with report owners so that they can complete the review on time.

Requirements

1. Deep and broad understanding of bank’s ecosystem, encompassing source systems, data landscape, operational processes and banking products across various segments.
2. Ability to build and maintain strong collaborative relationships across the organization – from technology and business units to support functions and customer segments – to facilitate efficient resolution of data quality issues and foster continuous improvement
3. Good knowledge of data quality principles
4. Proven experience in data issue management and data profiling
5. Excellent analytical, problem-solving, and decision-making skills
6. Strong communication skills with the ability to understand, simplify and explain complex data issues
7. Skilled in preparing effective and well-structured presentations with clear narratives and data analysis to varied audience
8. Thrives in a fast-paced, high intensity and ambiguous environment and possess a high level of intellectual curiosity
9. Demonstrated ability to thrive with minimal oversight, understand business needs and independently drive initiatives and cross functional engagements forward
10. Technical skills: SQL, Python

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