Data Analytics Manager - IA DART

What matters most

• Operationalizing the DART Framework

  The core mandate is to implement and scale the DART framework across all internal audit functions within the Singtel Group and its regional mobile associates. Success requires translating high-level audit strategies into actionable, automated data analytics and robotics workflows that function consistently across diverse global jurisdictions.

• Driving Continuous Risk Assessment and Auditing

  You must leverage AI and robotics to shift audit processes from periodic reviews to continuous, automated monitoring of high-risk areas. This requires designing robust data pipelines and analytical models that can identify anomalies in real-time, necessitating a deep integration of technical expertise with audit methodology.

• Stakeholder Influence and Data Sourcing

  A critical success factor is the ability to navigate a large, complex organization to secure access to disparate data sources. You must build strong relationships with cross-functional teams to gain cooperation, as the effectiveness of your analytics projects depends entirely on the quality and accessibility of the data you can source.

• Technical Mentorship and Capability Building

  Beyond individual technical delivery, you are responsible for upskilling the broader internal audit team. This involves creating training programs and providing hands-on coaching to ensure that non-specialist auditors can effectively utilize new analytical tools and interpret data-driven insights.

Likely challenges to think through

• Managing Global Stakeholder Diversity

  You will be influencing audit functions across multiple countries with varying operational standards. Prepare by demonstrating how you have navigated cultural or organizational silos to achieve consensus on technical standards, as you will need to balance global consistency with local operational realities.

• Balancing Technical Innovation with Audit Rigor

  Implementing AI and robotics in an audit context requires maintaining strict compliance and accuracy standards. The challenge is to innovate without compromising the integrity of the audit process; prepare to discuss how you validate automated models to ensure they meet the rigorous requirements of internal audit.

• Navigating Data Fragmentation

  Sourcing data across a large group like Singtel often involves dealing with legacy systems and inconsistent data formats. Prepare to discuss your experience in data cleaning, normalization, and building scalable ETL processes that can handle messy, real-world enterprise data.

Topics to prepare

• Continuous Auditing and Risk Assessment Frameworks

  The role requires operationalizing a DART framework to automate risk identification. Exercise: Draft a conceptual workflow for transitioning a manual audit process into a continuous, automated monitoring system using AI/Robotics, focusing on how to define high-risk triggers.

• Stakeholder Influence in Global Audit Environments

  You must influence cross-functional teams across multiple jurisdictions to source data. Exercise: Prepare a strategy for communicating the value of data analytics to non-technical audit stakeholders who may be resistant to changing traditional manual audit methodologies.

Skills to revise

• Robotic Process Automation (UiPath)

  The role specifically requires UiPath for audit automation. Exercise: Design a simple automation script that extracts data from a structured source and performs a validation check, simulating an automated audit control test.

• Data Visualization (Tableau/Power BI)

  You will need to present complex analytical findings to diverse groups. Exercise: Create a dashboard prototype that translates raw audit data into actionable insights, focusing on clear visual indicators for high-risk anomalies.

• Advanced Data Analytics (SQL/Python/R)

  Hands-on technical proficiency is required for complex projects. Exercise: Write a SQL query to join disparate audit datasets and a Python script to perform a basic anomaly detection analysis on financial or operational logs.

• Develop an Audit-Focused Analytics Case Study

  Prepare a project walkthrough demonstrating how you used SQL and Python/R to automate a manual audit process or risk assessment. Focus on how you identified high-risk areas and the specific impact of your data-driven insights on operational efficiency.

• Map Your Technical Stack to DART Framework Goals

  Create a summary document linking your hands-on experience in SQL, Tableau/Power BI, and UiPath to the DART framework's objectives of continuous auditing and robotics. Be prepared to explain how you select specific tools for different audit complexities.

• Prepare Examples of Cross-Functional Stakeholder Influence

  Draft two scenarios where you successfully sourced data from resistant or siloed departments. Highlight your communication strategy, how you managed expectations, and how you influenced stakeholders to adopt new analytical tools.

• Formulate Questions on Global Audit Integration

  Prepare thoughtful questions regarding how the DART team maintains consistency across regional mobile associates in different jurisdictions. Ask about the current challenges in scaling AI-driven audit solutions across diverse business units.

• Demonstrate Mentorship and Training Capabilities

  Outline a brief plan or methodology you have used to upskill non-technical team members in data analytics. Provide evidence of how you have coached others to improve their proficiency in BI tools or automated reporting.