The role is responsible for leading and operationalising a robust AI data governance and data management framework across the organisation's multiple operating markets, ensuring that a large customer base and high volume of transactions are supported by trusted, secure, and accessible data. The position treats data as a strategic enterprise asset, embedding policies, controls, and cultural practices that improve data quality, consistency, privacy, and usability, while enabling responsible AI, regulatory compliance, and stronger data driven decision making. The role requires close collaboration with data platform engineering, data science and AI/ML teams, risk, compliance and legal, information security, and business owners across markets to embed governance across the full data lifecycle and support the responsible monetisation of the organisation's data.

Responsibilities

Strategy Development and Implementation

Define, implement, and continuously evolve the enterprise AI data governance framework, policies, standards, and operating model across all markets.
Establish governance principles for data ownership, stewardship, and decision rights.
Establish governance principles for responsible AI and the ethical use of data.
Establish governance principles for federated delivery across operating markets, aligned to group standards.
Drive standardisation and reduce duplication across markets through shared policies and reusable governance components.

Data Governance and Stewardship

Establish and run governance bodies, such as a data governance council and data domain forums, with clear roles, decision rights, and escalation paths.
Operationalise data ownership and stewardship across business and technology domains.
Maintain data policies, standards, and a business glossary aligned to recognised data management frameworks.

Data Management and Quality

Define enterprise data quality standards, metrics, and remediation processes for critical data elements.
Drive master data, reference data, and metadata management to enable a single trusted view of customers, agents, and transactions.
Oversee data cataloguing and lineage so data is discoverable, documented, and traceable end to end.
Partner with data platform engineering to embed governance controls into pipelines, warehouses, and business intelligence layers.

AI and Model Governance (Responsible AI)

Establish controls for data used in AI/ML, including training data lineage, fitness for purpose, bias, and representativeness.
Define and enforce responsible AI principles, including fairness, transparency, explainability, and accountability, across the model lifecycle.
Implement model risk, model inventory, and ongoing monitoring controls in partnership with data science, risk, and compliance teams.
Track the evolving AI regulatory landscape and translate obligations into practical data and model governance requirements.

Privacy, Security and Regulatory Compliance

Embed privacy by design and data protection controls aligned to relevant data protection regulations.
Define and enforce data classification, access, retention, and minimisation standards with information security and legal teams.
Support regulatory reporting, audits, and examinations with reliable, well controlled data and evidence of governance.
Maintain alignment with recognised information security and payment card industry standards for the handling of sensitive financial and customer data.

Data Literacy, Value and Responsible Monetisation

Champion a data driven culture and raise data literacy through training, standards, and communities of practice.
Enable responsible monetisation by ensuring data products meet governance, privacy, consent, and ethical requirements.
Define guardrails for data sharing internally and with approved third parties.

Governance and Stakeholder Engagement

Participate in data and AI governance forums, providing governance leadership and guidance to major initiatives.
Support cross functional alignment across platform, data science, security, risk, product, and market teams.
Resolve data governance escalations relating to data quality and trust, privacy and regulatory compliance, AI or model risk posture, and delivery timelines.

Experience and education

Education

A four year tertiary degree in Computer Science, Data Science, Information Systems, or a related field is required.
A Master's degree or MBA is advantageous.

Certifications (advantageous)

Certified Data Management Professional or equivalent.
A recognised data governance certification.
A cloud certification relevant to data engineering or analytics.
A project or change management certification.

Experience

5 to 8 years of experience in data governance, data management, or enterprise data roles.
Proven experience implementing data governance frameworks in large, regulated environments, preferably within financial services.
Strong understanding of data platforms, business intelligence, analytics, and the AI/ML implications for governance.
Demonstrated success leading cross functional, multi country data initiatives.
Experience across multi market or federated organisational structures is strongly advantageous.
Strong stakeholder management skills, with the ability to translate complex concepts for executive and non technical audiences.

Technical Competencies

Familiarity with data governance and master data management tools.
Familiarity with data platforms, including SQL based and cloud native data warehouses.
Knowledge of privacy and compliance frameworks and information security standards.
Familiarity with business intelligence and analytics tools.
Understanding of AI/ML governance concepts, including model lineage, bias, and explainability.
Programming and data science skills, such as Python, R, and SQL, are desirable.

Behavioural Qualities

Analytical and detail oriented.
Business oriented and value driven.
Accountable, decisive, and resilient under pressure.
Collaborative, inclusive, and respectful.
Strong ethical and governance mindset.