New opportunity

Data Governance Specialist

Adecco · Singapore

About this role

The Data Governance Transformation Associate will play a key role in transforming and operationalising the Bank’s data governance processes. This role combines data governance, workflow automation, AI agent development and stakeholder management, with responsibility for progressively automating existing Data Management and Governance activities and establishing scalable, technology-enabled ways of working. The candidate will be responsible for transforming existing Data Management and Governance activities using automation and AI agents and continue to manage the agents of these activities. The incumbent will build, deploy and manage agents that support governance processes, including monitoring their operation, managing exceptions and continuously improving their effectiveness. Key responsibilities include: Operationalise and expand data governance activities across Critical Reports, Critical Data Elements (CDEs) and key business processes. Manage core governance activities including data quality monitoring, issue management and reporting to management committee members. Drive periodic reviews of Critical Reports, CDEs, business definitions, ownership, business rules and metadata. Identify and automate manual data management and governance workflows to improve efficiency and scalability. Design, build, deploy and manage AI agents to execute and monitor governance workflows, including appropriate controls and human oversight. Partner with stakeholders across all functions in the Bank to embed data governance requirements into BAU processes. Requirements: Bachelor’s degree in Computer Science, Information Systems, Data Science, Business Analytics, Accountancy or a related discipline. 5+ years of relevant experience in data governance, data management, process automation, AI solutions or related areas, preferably within financial services. Strong understanding of data governance principles, including data quality, metadata management, data ownership, critical data elements, data lineage, controls and issue management. Hands-on experience in workflow automation and AI/agent development, with the ability to design, build, deploy, monitor and continuously improve automated or agent-enabled workflows; experience with Microsoft Power Platform and Copilot Studio would be advantageous. Strong stakeholder management and communication skills. This is a 12-month contract position based in the Singapore CBD.

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

Skills for this role

Data GovernanceData ManagementWorkflow AutomationAI Agent DevelopmentStakeholder ManagementData Quality MonitoringMetadata ManagementData LineageMicrosoft Power PlatformCopilot Studio

Checking your profile…

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

Understand the role

What matters most

Operationalizing Data Governance via Automation
The core mandate is to transition manual data management tasks into automated workflows. You must prioritize identifying bottlenecks in existing processes—such as CDE reviews or quality monitoring—and replacing them with scalable, technology-enabled solutions.
AI Agent Lifecycle Management
Beyond building AI agents, you are responsible for their ongoing performance. This requires establishing robust monitoring frameworks, managing exception handling, and ensuring continuous improvement of agent effectiveness within the bank's governance ecosystem.
Cross-Functional Stakeholder Alignment
You must bridge the gap between technical governance requirements and business-as-usual (BAU) processes. Success depends on your ability to partner with diverse functions like Risk, Compliance, and Technology to ensure governance controls are embedded without disrupting operational efficiency.

Likely challenges to think through

Balancing AI Autonomy with Regulatory Control
In a banking environment, deploying AI agents for governance introduces risks regarding accuracy and compliance. You will face the challenge of designing agents that are efficient while maintaining strict human oversight and audit trails. Prepare by demonstrating how you implement 'human-in-the-loop' controls within automated workflows.
Legacy Process Integration
Transforming established, manual data management activities often meets resistance from teams accustomed to legacy workflows. You may struggle to gain buy-in for new automated tools. Prepare by articulating a strategy for change management and demonstrating how to translate technical governance needs into practical, value-add solutions for business users.

Practice your answers

Technical practice

Data Governance Frameworks

What to coverExplain how you would operationalise data governance for Critical Data Elements (CDEs). Focus on your approach to defining ownership, establishing business rules, and ensuring data lineage is accurately captured.

AI Agent Lifecycle Management

What to coverDescribe your process for building and deploying AI agents to automate governance workflows. Address how you handle monitoring, exception management, and continuous improvement of agent performance.

Workflow Automation Strategy

What to coverDiscuss your methodology for identifying manual data management tasks suitable for automation. Explain how you balance efficiency gains with the need for appropriate controls and human oversight.

Data Quality Monitoring

What to coverHow do you design and implement automated data quality checks within a financial services environment? Suggest how to translate business requirements into technical monitoring rules.

Metadata and Business Definitions

What to coverExplain the importance of maintaining metadata and clear business definitions in a large-scale banking environment. Describe how you ensure these remain accurate during periodic reviews.

Microsoft Power Platform Integration

What to coverIf you have experience with Power Platform or Copilot Studio, describe a specific workflow you automated. Focus on the technical components used and how you ensured the solution was scalable.

Issue Management and Reporting

What to coverDescribe your approach to managing data governance issues and reporting them to management committees. Suggest how to present technical data quality metrics in a way that is actionable for non-technical stakeholders.

Controls and Compliance

What to coverHow do you embed governance controls into Business-As-Usual (BAU) processes? Discuss the trade-offs between strict compliance requirements and operational agility.

Behavioural practice

Stakeholder Alignment

What to coverDescribe a situation where you had to convince stakeholders from different functions (e.g., Risk, Compliance, Technology) to adopt a new data governance process. How did you address their competing priorities?

Managing Automation Failures

What to coverProvide an example of when an automated process or AI agent you managed failed or produced an error. How did you identify the issue, mitigate the impact, and prevent recurrence?

Translating Requirements

What to coverGive an example of a time you had to translate complex governance requirements into a practical, technical solution. How did you ensure the final output met the business needs?

Driving Process Improvement

What to coverDescribe a time you identified a manual, inefficient workflow and successfully automated it. What was the impact on the team's productivity and data accuracy?

Sharpen your knowledge

Topics to prepare

Data Governance Frameworks in Banking
Review the lifecycle of Critical Data Elements (CDEs) and data lineage within a financial services context. Exercise: Draft a process map for identifying and validating a new CDE, including the necessary business definitions and ownership assignment, to demonstrate your understanding of how governance is operationalized.
AI Agent Lifecycle Management
Understand the stages of designing, deploying, monitoring, and refining AI agents for business workflows. Exercise: Create a conceptual framework for an agent that monitors data quality exceptions, focusing on how you would implement human-in-the-loop controls and continuous improvement loops.

Skills to revise

Workflow Automation with Microsoft Power Platform
Develop proficiency in automating manual data management tasks. Exercise: Build a mock Power Automate flow that triggers an alert or report update based on a simulated data quality issue, ensuring you can explain how this improves scalability.
AI Agent Development with Copilot Studio
Gain hands-on experience in building conversational or task-based agents. Exercise: Design a simple agent prototype in Copilot Studio that can retrieve metadata or business rules from a repository, focusing on the configuration of triggers and actions.
Stakeholder Management in Governance
Practice translating technical governance requirements for non-technical business partners. Exercise: Prepare a communication plan for introducing a new automated data quality check to a business unit, focusing on addressing potential resistance and explaining the operational benefits.

Your preparation checklist

Map Data Governance Principles to Automation
Prepare a summary of how you have applied data governance frameworks—specifically regarding Critical Data Elements (CDEs), data lineage, and quality monitoring—within a financial services context. Be ready to explain how you would translate these manual governance tasks into automated workflows.
Develop an AI Agent Project Walkthrough
Create a brief case study of an AI agent or automation tool you have built. Focus on the lifecycle: design, deployment, monitoring for exceptions, and the specific human-in-the-loop controls you implemented to ensure accuracy and compliance.
Prepare Technical Proficiency Evidence
Document your experience with Microsoft Power Platform and Copilot Studio. If you have not used these specific tools, prepare examples of similar low-code or AI agent development platforms, highlighting your ability to scale governance processes.
Formulate Stakeholder Management Scenarios
Prepare examples of how you have successfully navigated cross-functional collaboration between Business, Risk, and Technology teams. Focus on how you communicated complex governance requirements to non-technical stakeholders to embed them into daily operations.
Draft Targeted Questions for the Interviewer
Prepare questions regarding the bank's current maturity level in data governance, the specific types of manual workflows they are prioritizing for automation, and how the team currently balances AI agent autonomy with necessary human oversight.

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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