New opportunity

Lead, Data & AI Governance

WhiteCrow · Singapore

About this role

The Lead, Data & AI Governance will define and execute the Group-wide strategy, framework, and operating model for data and AI governance. This senior role is responsible for establishing policy architecture, ensuring regulatory compliance, and driving a culture of data and AI discipline across the organization. Responsibilities include: Strategy & Policy: Defining the governance roadmap, authoring policies, and monitoring adherence. Governance Operations: Establishing decision rights (RACI) and stewardship roles. Data Management: Setting standards for data ownership, metadata, quality, and master data management (MDM). Risk & Compliance: Partnering with Cybersecurity and Legal to manage data protection, sharing, and retention, while conducting regulatory horizon scanning and AI risk assessments. Leadership: Reporting governance KPIs to executive stakeholders and acting as a change agent. Requirements: Bachelor's degree in a relevant field (postgraduate qualification preferred). At least 8 years of experience in data management, with 5+ years leading Data Governance, Data Quality, or MDM programs in regulated environments. Extensive knowledge of AI regulatory requirements and experience in AI/model governance, including generative AI. Strong technical fluency in SQL and Python, with experience in modern data catalogues (e.g., DataHub, Microsoft Purview), data contracts, and Git-based delivery. Relevant certifications like CDMP, CIMP, or CISA are strongly preferred.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Data GovernanceData Privacy & ProtectionData Quality ManagementAI Risk ManagementRegulatory ComplianceMaster Data ManagementSQLPythonDataHubMicrosoft PurviewGit

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Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Establishment of Group-wide Governance Framework
The primary objective is to design and implement a comprehensive strategy for both data and AI governance. This requires creating a policy architecture, defining RACI matrices, and establishing stewardship roles across diverse business units to ensure consistent data discipline.
AI and Model Risk Management
The role demands active management of AI-specific risks, including generative and potentially agentic AI. You must conduct regulatory horizon scanning and perform AI risk assessments to ensure that all AI deployments align with evolving global standards and internal compliance requirements.
Operationalizing Data Quality and MDM
You are expected to act as the design authority for Master Data Management (MDM) and oversee the data quality lifecycle. This involves setting clear standards for metadata, data contracts, and lineage, while ensuring remediation processes are enforced through measurable KPIs.
Change Management and Stakeholder Engagement
As a senior liaison between business and technology, you must drive a culture of data discipline. Success depends on your ability to translate complex governance requirements into actionable business insights for executive stakeholders and influence cross-functional teams.

Likely challenges to think through

Balancing Innovation with Strict Controls
A significant challenge is implementing robust access governance and data contracts without stifling business innovation. Prepare by demonstrating how you have successfully integrated governance checks into agile delivery lifecycles, such as Git-based workflows, without creating bottlenecks.
Navigating Multi-Jurisdictional Compliance
Operating in a global aviation context involves complex, varying regulatory landscapes. You will face the difficulty of harmonizing policies across different regions. Prepare by discussing your experience in regulatory horizon scanning and how you adapt global frameworks to local legal requirements.
Technical Credibility in a Senior Role
The role requires high-level strategic leadership alongside hands-on technical fluency in SQL, Python, and modern catalogues like DataHub or Purview. You must be prepared to bridge the gap between high-level policy authoring and the technical implementation of policy-as-code.

Practice your answers

Technical practice

Designing a Data Governance Operating Model

What to coverExplain how you would establish a RACI matrix and define stewardship roles across diverse business units. Focus on how you ensure accountability while maintaining operational agility in a large, multi-entity organization.

Implementing AI and Model Governance

What to coverDescribe your approach to governing generative AI and agentic AI models. Address how you manage model risk, ensure transparency, and maintain compliance with evolving AI regulatory standards.

Data Quality Frameworks and SLAs

What to coverHow do you define and enforce data quality dimensions and thresholds? Discuss your process for setting up a remediation loop and reporting accountability to stakeholders.

Master Data Management (MDM) Strategy

What to coverExplain your methodology for centralizing master and reference data. Address the challenges of securing design ratification across disparate systems and scaling MDM capabilities.

Data Contracts and Lineage

What to coverHow do you integrate data contracts and lineage coverage into the software delivery lifecycle? Explain the technical benefits of embedding these governance checks into Git-based delivery pipelines.

Regulatory Horizon Scanning

What to coverDescribe your process for conducting regulatory horizon scanning across multiple jurisdictions. How do you translate these findings into actionable policy updates and control re-baselining?

Access Governance and Security

What to coverDiscuss how you balance data innovation with strict access controls. Explain your experience with policy-as-code access models and how they facilitate secure data sharing.

Technical Proficiency in Data Catalogues

What to coverCompare the use of modern data catalogues like DataHub or Microsoft Purview in a governance program. Explain how you leverage these tools to manage metadata and dataset certification.

Behavioural practice

Driving Cultural Change in Governance

What to coverProvide an example of how you acted as a change agent to instill a culture of data discipline. Describe the situation, the resistance you encountered, the actions you took to influence stakeholders, and the measurable result.

Managing Executive Stakeholder Expectations

What to coverDescribe a time you had to present complex governance KPIs to executive leadership. How did you translate technical metrics into business-relevant insights to secure their support or budget?

Resolving Conflicts in Data Ownership

What to coverShare a situation where you had to mediate a disagreement between business units regarding data ownership or master data design. What was your approach to brokering a consensus?

Handling Regulatory Non-Compliance

What to coverTell me about a time you identified a significant data protection or compliance risk. How did you manage the escalation process and ensure the mitigation was tracked to closure?

Sharpen your knowledge

Topics to prepare

AI and Model Governance Frameworks
Review the lifecycle of AI model governance, specifically focusing on risk assessment, model validation, and responsible AI practices. Exercise: Draft a high-level governance framework for a generative AI application that includes model risk assessment criteria and human-in-the-loop oversight mechanisms. This is directly relevant to the requirement for managing AI/model governance and generative AI risks.
Data Governance Operating Models
Study organizational structures for data governance, including RACI matrices and stewardship models. Exercise: Design a RACI matrix for a hypothetical data quality remediation project, defining roles for Data Owners, Data Stewards, and IT stakeholders. This addresses the requirement to establish decision rights and stewardship roles across business units.
Regulatory Horizon Scanning
Understand the process of monitoring evolving data and AI regulations across multiple jurisdictions. Exercise: Create a summary table of current AI-specific regulations in major regions (e.g., EU AI Act, Singapore's Model AI Governance Framework) and identify potential impacts on corporate data policies. This is essential for the role's responsibility to conduct regulatory horizon scanning.

Skills to revise

Data Contracts and Lineage
Practice defining data contracts that specify schema, quality, and semantic requirements. Exercise: Write a sample data contract in YAML format for a critical dataset, including metadata fields and quality thresholds. This aligns with the requirement to mandate data contracts and embed governance checks into the delivery lifecycle.
Git-based Policy Delivery
Familiarize yourself with managing policies as code using version control systems. Exercise: Create a repository structure in Git to manage a set of data access policies, including a pull request workflow for policy updates. This is relevant to the requirement for Git/pull-request-based delivery of governance standards.
SQL for Data Quality Auditing
Use SQL to perform data quality checks and identify anomalies. Exercise: Write SQL queries to detect null values, duplicate records, or out-of-range values in a sample dataset to generate a quality scorecard. This supports the requirement for technical fluency in SQL to oversee data quality operating loops.

Your preparation checklist

Develop an AI Governance Framework Case Study
Prepare a presentation or document outlining how you would design an AI governance operating model, specifically addressing generative AI risks and model lifecycle management. Include how you would define RACI matrices and establish oversight for agentic AI.
Demonstrate Technical Proficiency in Data Governance Tools
Prepare a walkthrough of your experience using modern data catalogues like DataHub or Microsoft Purview. Be ready to explain how you have implemented data contracts, automated lineage, and Git-based delivery workflows for policy-as-code.
Prepare Evidence of Regulatory Compliance Strategy
Draft a summary of your approach to regulatory horizon scanning in a multi-jurisdictional environment. Explain how you translate complex legal requirements into actionable data policies and how you conduct Key Risk and Control Self-Assessments (KRCSA).
Formulate Strategic Questions for Leadership
Prepare thoughtful questions regarding the current maturity level of their data quality scorecards, the biggest challenges in scaling their MDM platform, and how the organization currently balances AI innovation with data protection requirements.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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