New opportunity

Data Analyst - Data Literacy & Governance (Assistant Vice President)

Bank of Singapore · Singapore

About this role

The Data Analyst (Assistant Vice President) 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 to automate existing data management activities and establish scalable, technology-enabled workflows. The incumbent will build, deploy, and manage AI agents to support governance processes, including monitoring operations, managing exceptions, and improving effectiveness. Additionally, the role involves pioneering the Bank's enterprise ontology to create a semantic foundation for data discovery and future AI use cases. Key responsibilities include: Operationalise and expand data governance across Critical Reports and Critical Data Elements (CDEs); Manage core governance activities such as data quality monitoring and issue management; Identify and automate manual workflows; Design, build, and deploy AI agents with appropriate human oversight; Partner with stakeholders to embed governance into business processes; Establish the enterprise ontology by defining business concepts and relationships. Requirements: Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, Business Analytics, or a related field. Minimum 8 years of relevant experience in data governance, data management, process automation, or AI solutions, preferably within financial services. Strong understanding of data governance principles (metadata, lineage, controls). Hands-on experience in workflow automation and AI/agent development. Familiarity with agentic AI, APIs, and low-code platforms (Microsoft Power Platform and Copilot Studio are advantageous). Knowledge of ontology, knowledge graphs, or semantic modelling. Strong stakeholder management and communication skills are essential.

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 DevelopmentAgentic AIOntologyKnowledge GraphsSemantic ModellingMetadata ModellingAPIsWorkflow OrchestrationLow-code/No-code PlatformsMicrosoft Power PlatformCopilot StudioData Quality MonitoringStakeholder Management

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
The primary focus is to transform and scale data governance processes, specifically for Critical Reports and Critical Data Elements (CDEs). You must move beyond manual oversight to establish technology-enabled, automated workflows that ensure data quality and effective issue management across the bank.
AI Agent Development and Lifecycle Management
You are expected to design, build, and deploy AI agents to execute governance tasks. This requires not just technical implementation, but also ongoing management, monitoring for exceptions, and continuous improvement of agent performance within a controlled, human-supervised environment.
Establishing Enterprise Ontology
You must pioneer the bank's enterprise ontology by mapping business concepts, relationships, and semantic standards to data, systems, and controls. This foundational work is critical for improving data discovery and enabling future AI and agentic use cases.
Cross-Functional Stakeholder Engagement
Success depends on your ability to partner with Business, Risk, Compliance, and Technology teams. You must translate complex governance requirements into practical, embedded business processes while navigating the organizational structure to drive adoption.

Likely challenges to think through

Balancing Automation with Regulatory Controls
In a financial services environment, automating governance workflows requires strict adherence to controls. The challenge is to design AI agents that are efficient yet transparent and auditable. Prepare by demonstrating how you have implemented 'human-in-the-loop' oversight in previous automation projects.
Navigating Ambiguity in Ontology Design
Establishing an enterprise-wide ontology is a complex, abstract task that requires reconciling different definitions across diverse business units. You may face resistance or conflicting requirements. Prepare by articulating a structured methodology for semantic modeling and how you build consensus among stakeholders.
Integrating Legacy Processes with New AI Capabilities
Transitioning from manual, legacy governance to AI-enabled workflows is difficult. You will likely encounter resistance to change or technical debt. Prepare by discussing your experience in change management and how you identify 'quick wins' to demonstrate the value of automation to skeptical business partners.

Practice your answers

Technical practice

Data Governance Frameworks in Financial Services

What to coverDiscuss how you would operationalize data governance for Critical Data Elements (CDEs) and Critical Reports. Focus on your approach to defining data ownership, establishing lineage, and implementing controls within a highly regulated banking environment.

Designing and Deploying AI Agents

What to coverExplain your methodology for building and deploying AI agents to automate governance workflows. Address how you incorporate human-in-the-loop oversight, manage exceptions, and ensure the continuous monitoring and improvement of agent performance.

Enterprise Ontology and Semantic Modelling

What to coverDescribe your experience in establishing an enterprise ontology. Explain how you map business concepts, processes, and systems to create a semantic foundation that supports data discovery and future AI use cases.

Workflow Automation Strategy

What to coverHow do you identify manual data management tasks suitable for automation? Discuss your experience with low-code/no-code platforms like Microsoft Power Platform and how you balance efficiency with the need for robust governance controls.

Data Quality Monitoring and Issue Management

What to coverDetail your approach to managing data quality issues in a large-scale financial institution. Explain how you would automate the detection of quality breaches and the subsequent escalation or remediation workflows.

Integration of APIs and Workflow Orchestration

What to coverDiscuss the role of APIs and workflow orchestration in connecting disparate banking systems. Provide an example of how you have used these tools to create scalable, technology-enabled governance processes.

Metadata Management and Lineage

What to coverExplain the importance of metadata management in the context of data governance. How do you ensure that metadata remains accurate and useful for both human stakeholders and automated AI agents?

Knowledge Graphs for Data Discovery

What to coverHow can knowledge graphs be leveraged to improve data discovery within a bank? Discuss the trade-offs between traditional relational databases and graph-based approaches for representing complex business relationships.

Behavioural practice

Stakeholder Management Across Functions

What to coverDescribe a time you had to align stakeholders from Business, Risk, Compliance, and Technology on a data governance initiative. How did you translate complex technical requirements into practical solutions that met everyone's needs?

Navigating Ambiguity in Projects

What to coverProvide an example of a project where you had to take an idea from concept to implementation with little initial guidance. How did you define the scope, manage expectations, and ensure successful adoption?

Driving Cultural Change in Data Literacy

What to coverShare an experience where you had to embed new data governance requirements into existing business-as-usual (BAU) processes. How did you overcome resistance and ensure long-term compliance and engagement?

Proactive Problem Solving and Builder Mindset

What to coverTell us about a time you identified a significant inefficiency in a data management process. What steps did you take to automate or improve it, and what was the measurable impact on the organization?

Sharpen your knowledge

Topics to prepare

Data Governance Frameworks in Banking
Review the lifecycle of Critical Data Elements (CDEs) and Critical Reports. Exercise: Map a hypothetical business process to its underlying data lineage and identify potential control points for automated monitoring. This is relevant to the role's core responsibility of operationalizing governance across the bank.
Enterprise Ontology and Semantic Modeling
Study the principles of defining business concepts and their relationships within a knowledge graph. Exercise: Create a conceptual model linking a business process, a data element, and a regulatory control. This is essential for the role's task of pioneering the bank's enterprise ontology.
Agentic AI and Human-in-the-loop Systems
Understand the architecture of AI agents designed for workflow automation, specifically focusing on exception management and oversight. Exercise: Design a flowchart for an AI agent that monitors data quality, including the specific triggers for human intervention. This aligns with the requirement to build and deploy agents with appropriate controls.

Skills to revise

Workflow Automation with Low-Code Platforms
Practice building automated workflows using tools like Microsoft Power Platform. Exercise: Create a prototype workflow that triggers an alert or action based on a data quality exception. This directly addresses the requirement for hands-on experience in automating manual governance tasks.
Stakeholder Management for Governance
Develop techniques for translating technical governance requirements into business-friendly language. Exercise: Draft a communication plan for introducing a new automated data quality control to non-technical business stakeholders. This is critical for the role's requirement to embed governance into business-as-usual processes.

Your preparation checklist

Map Data Governance to Automation
Prepare a summary of your experience applying data governance principles (e.g., CDEs, lineage, quality monitoring) to manual processes. Be ready to explain how you identified bottlenecks and used automation or AI agents to improve efficiency while maintaining strict controls.
Develop an Ontology Case Study
Create a brief walkthrough of a project where you defined business concepts and their relationships. Demonstrate how you used semantic modeling or knowledge graphs to improve data discovery or system interoperability, as this is central to the bank's enterprise ontology initiative.
Prepare AI Agent Deployment Examples
Document your hands-on experience building and deploying AI agents. Focus on the lifecycle: design, deployment, monitoring for exceptions, and implementing human-in-the-loop oversight. Highlight familiarity with low-code platforms like Microsoft Power Platform or Copilot Studio if applicable.
Formulate Stakeholder Management Scenarios
Prepare examples of how you have translated complex governance requirements into practical solutions for non-technical stakeholders across Risk, Compliance, and Business units. Focus on your ability to navigate ambiguity and drive adoption of new processes.
Draft Strategic Questions for the Interview
Prepare thoughtful questions regarding the current maturity of the bank's enterprise ontology, the specific governance workflows currently prioritized for automation, and how the team balances AI agent autonomy with the rigorous control requirements of a financial institution.

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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