New opportunity

Senior Manager, Group Finance - Systems & Analytics

Frasers Property · Singapore

About this role

The Senior Manager, Group Finance - Systems & Analytics will play a pivotal role in building the Group's finance data, analytics, and AI capabilities. This hands-on position involves designing and implementing an Enterprise Finance Data Lake and AI platform, establishing data governance frameworks, and enabling AI-powered solutions to enhance reporting, decision support, and process automation. Responsibilities include: Finance Data Platform & Governance: Design and manage the finance data platform, build scalable data pipelines, and enforce data quality and governance standards. Data Modelling & Semantic Layer: Develop enterprise finance data models and semantic layers to ensure a single source of truth. Analytics & Reporting: Develop dashboards and analytics solutions to drive business insights and automate reporting. AI Solutions & Intelligent Automation: Implement AI-powered finance solutions, including Generative AI and AI agents, while establishing MLOps practices for model deployment and monitoring. Stakeholder Management: Partner with Finance, IT, and business stakeholders to deliver scalable solutions aligned with business priorities. Qualifications: A Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Accountancy, or Finance is required, along with a minimum of 7 years of relevant experience in data engineering, architecture, or AI solution development. Candidates must possess strong hands-on skills in SQL and Python. Preferred qualifications include experience with cloud platforms like GCP, BigQuery, or Databricks, as well as domain knowledge in financial reporting, FP&A, and enterprise systems like SAP S/4HANA or Workday. The role offers the opportunity to shape the future of finance data and AI strategy within a multinational real estate group.

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

Skills for this role

SQLPythonData EngineeringData ArchitectureData ModellingData GovernanceGenerative AIAI AgentsMLOpsBusiness IntelligenceCloud Data PlatformsBigQueryDatabricksMicrosoft FabricAzure Data LakeFinancial ReportingFP&ASAP S/4HANAOracleWorkdayOneStream

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

Enterprise Finance Data Platform Architecture
The core priority is designing and managing a scalable finance data lake and semantic layer. This requires building robust data pipelines and establishing a single source of truth for financial metrics to ensure data consistency across the multinational group.
AI and Intelligent Automation Implementation
The role must drive the adoption of Generative AI and AI agents within the finance function. This involves establishing MLOps practices for model deployment, monitoring, and ensuring that AI solutions are reliable, scalable, and directly improve financial reporting and decision support.
Finance Data Governance and Quality
Establishing and enforcing strict data governance, metadata management, and master data standards is essential. The candidate must ensure that all financial data is trusted, secure, and compliant to support high-stakes reporting and analytics.
Cross-Functional Stakeholder Alignment
Success depends on bridging the gap between Finance, IT, and business units. The role requires translating complex business requirements into technical solutions while promoting the adoption of new data and AI capabilities across the organization.

Likely challenges to think through

Navigating Complex Legacy Finance Systems
Integrating data from diverse enterprise systems like SAP S/4HANA or Workday into a unified data lake presents significant technical hurdles. Prepare by demonstrating experience in mapping complex legacy data structures to modern cloud-based semantic models.
Balancing Technical Innovation with Financial Rigor
Implementing AI in a finance context requires balancing cutting-edge technology with the need for extreme accuracy and auditability. Prepare by discussing how you maintain MLOps standards that prioritize reliability and explainability in financial outputs.
Managing Organizational Change and Adoption
Introducing AI-powered automation and new data platforms may face resistance from traditional finance teams. Prepare by highlighting strategies for stakeholder engagement, demonstrating the tangible value of automation, and providing training to ensure user adoption.

Practice your answers

Technical practice

Designing an Enterprise Finance Data Lake

What to coverExplain your approach to architecting a scalable data lake for finance, specifically addressing how you handle data ingestion from disparate ERP systems like SAP S/4HANA or Workday. Focus on your strategy for maintaining data lineage, security, and the integration of raw data into curated, analytics-ready layers.

Implementing a Semantic Layer for Finance

What to coverDescribe how you would design a semantic layer to ensure a single source of truth for financial KPIs across a multinational group. Discuss the trade-offs between centralized versus decentralized modeling and how you ensure business logic remains consistent for both reporting and AI-driven use cases.

Data Governance in Financial Systems

What to coverHow do you establish and enforce data quality and master data management standards in a complex, multi-entity environment? Suggestion: Focus on your experience with metadata management and the processes you use to reconcile data discrepancies between operational and financial reporting systems.

Scalable Data Pipeline Development

What to coverDetail your experience building and maintaining high-volume data pipelines using Python and SQL. Explain how you handle error logging, data quality checks, and performance optimization when dealing with large-scale financial datasets.

MLOps for Finance AI Solutions

What to coverDescribe your framework for deploying and monitoring AI models in a production finance environment. Address how you manage model drift, version control, and the integration of MLOps practices to ensure AI-powered automation remains reliable and auditable.

Generative AI and AI Agents in Finance

What to coverHow would you identify and implement high-impact Generative AI or agentic workflows for financial analysis or document processing? Discuss the technical requirements for building the necessary knowledge foundations and how you mitigate risks like hallucinations in financial reporting.

Cloud Data Platform Strategy

What to coverCompare the strengths of platforms like BigQuery, Databricks, or Microsoft Fabric for a finance-focused data architecture. Suggestion: Discuss how you would select a platform based on requirements for security, cost-efficiency, and native integration with existing enterprise systems.

Automating Financial Reporting

What to coverExplain your methodology for transitioning manual reporting processes into automated, self-service analytics solutions. Focus on how you balance the need for standardized metrics with the flexibility required by different business units.

Behavioural practice

Bridging Finance and IT Stakeholders

What to coverDescribe a situation where you had to translate complex business requirements from Finance into a technical solution for IT teams. Explain how you managed conflicting priorities and ensured the final delivery met the needs of both groups.

Driving Finance Transformation

What to coverGive an example of a time you led a change initiative to adopt new data or AI capabilities within a finance function. How did you handle resistance to change and ensure the adoption of the new technology by non-technical finance staff?

Managing Vendor and Partner Relationships

What to coverDescribe a project where you worked closely with external technology vendors or implementation partners. How did you ensure the quality of their deliverables and maintain alignment with the company's long-term data strategy?

Prioritizing High-Impact AI Initiatives

What to coverTell me about a time you had to prioritize multiple potential AI or analytics projects with limited resources. What criteria did you use to evaluate their business impact, and how did you communicate your decision to stakeholders?

Sharpen your knowledge

Topics to prepare

Enterprise Finance Data Architecture
Review principles of designing a modern finance data lake and semantic layer. Practice mapping complex financial reporting requirements to scalable data structures that ensure a single source of truth. This is critical for the role's mandate to build the Group's finance data platform.
Finance AI Governance and MLOps
Study frameworks for AI governance, specifically focusing on model deployment, monitoring, and lifecycle management within a financial context. Prepare to discuss how to maintain reliability and security for Generative AI and AI agents in a corporate environment.
Financial Planning and Analysis (FP&A) Integration
Refresh knowledge on FP&A workflows, consolidation, and management reporting. Understanding these business processes is essential for translating stakeholder needs into effective data models and automated analytics solutions.

Skills to revise

Advanced SQL and Python for Data Engineering
Practice writing complex SQL queries for data transformation and Python scripts for building scalable data pipelines. The role requires hands-on development of data integration processes across diverse finance systems.
Cloud Data Platform Implementation
Gain familiarity with cloud-based analytics environments such as Google Cloud Platform (GCP), BigQuery, or Databricks. Focus on how these platforms support large-scale data ingestion and the deployment of AI-ready datasets.
Data Modelling for Enterprise Systems
Exercise designing semantic layers and data models that integrate with enterprise systems like SAP S/4HANA or Workday. This skill is vital for ensuring data consistency and accessibility for self-service reporting.

Your preparation checklist

Architect a Finance Data Lake Strategy
Prepare a conceptual framework for an Enterprise Finance Data Lake. Be ready to discuss how you would integrate data from ERP systems like SAP S/4HANA or Workday into a cloud environment (e.g., GCP/BigQuery or Databricks) while maintaining data quality and governance standards.
Develop a Semantic Layer Case Study
Prepare a walkthrough of a project where you designed a semantic layer or enterprise data model. Focus on how you standardized KPI definitions and business logic to create a single source of truth for financial reporting and FP&A stakeholders.
Demonstrate MLOps and AI Implementation
Outline your experience with the full lifecycle of an AI or automation project. Specifically, explain how you handle model deployment, monitoring, and MLOps practices to ensure that Generative AI or AI agents remain reliable and scalable in a production finance environment.
Prepare Stakeholder Management Examples
Identify specific instances where you bridged the gap between technical IT teams and non-technical finance business users. Prepare to explain how you translated complex business requirements into scalable data products and managed expectations during implementation phases.
Formulate Strategic Questions for the Interview
Prepare thoughtful questions regarding the current maturity of Frasers Property's finance data infrastructure, the primary pain points in their existing reporting processes, and how the team balances immediate automation needs with long-term AI strategy.

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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