Description

Principal Accountabilities and Responsibilities

For Business Customers and Stakeholders, internal control environment etc.

• Assess, monitor and improve data quality across critical data sets to ensure fitness-for-purpose for business decisioning, customer outcomes, analytics, operational processing and regulatory reporting.
• Translate business requirements and control obligations into measurable data quality rules, thresholds and control checks eg. completeness, accuracy, validity, consistency, timeliness, uniqueness.
• Define and maintain Data Quality (DQ) control frameworks including rule inventory, control design, monitoring frequency, ownership RACI, evidencing and exception handling.
• Partner with Data Owners, Data Stewards, Technology and Operations teams to identify root causes of data issues, agree remediation plans and drive sustainable fixes, not just tactical cleanups.
• Establish and govern DQ scorecards, dashboards and KPI/KRIs to provide transparent reporting to stakeholders including senior management and governance forums.
• Manage the DQ issue lifecycle: logging, triage, impact assessment, remediation tracking, retesting, closure, evidence and lessons learned.
• Contribute to defining and embedding data standards and business rules that prevent defects upstream, shift-left quality.
• Collaborate with Metadata / Governance teams to align DQ rules with the Business Data Glossary, critical data elements (CDEs), lineage and trusted sources.
• Onboard and maintain critical data elements and DQ rules within strategic governance tooling eg. Collibra, ensuring traceability from requirement → rule → metric → issue → remediation.
• Promote a continuous-improvement culture by standardising reusable DQ patterns, automation opportunities and improved ways of working across teams and geographies.

Functional Knowledge

• 6–10 years of relevant experience in Data Quality, Data Governance, Data Controls, Data Management, MI/Reporting controls or related roles.
• Bachelors or Masters degree or equivalent in a numerate discipline such as Computer Science, Engineering, Information Systems, Mathematics, Statistics or similar.
• Strong understanding of data quality dimensions and control design including how to operationalise them into rules, monitoring and evidence.
• Hands-on experience with data profiling, rule definition, exception analysis and root-cause investigation across complex data flows.
• Working knowledge of metadata management and data governance concepts, data ownership/stewardship, CDEs, glossary, lineage, trusted sources; experience using Collibra or equivalent for DQ and governance is highly desirable.
• Strong SQL skills and experience analysing large datasets; familiarity with data platforms eg. relational databases, data lakes/warehouse and common data integration patterns.
• Exposure to DQ tooling and/or automation approaches eg. Collibra DQ, Informatica DQ, DataStage/ETL checks, Python-based frameworks or comparable.
• Understanding of banking/financial-services data operating models and the control environment in a global bank; ability to link DQ issues to business impacts, customer servicing, risk, finance, regulatory compliance.
• Awareness of regulatory and risk-driven data expectations eg. BCBS239 and broader regulatory reporting principles and experience supporting audit/regulatory evidence packs.
• Familiarity with data management and architecture frameworks such as DAMA, TOGAF, FIBO or EDM; understanding of how DQ fits into end-to-end data lifecycle management.
• Strong stakeholder management skills; able to work across business, operations and technology, confident in facilitating workshops, challenging constructively and gaining agreement on rule definitions and remediation ownership

Key Skills

Education

Any Graduate