Data Auditor

The Data Auditor supports the Internal Audit function by applying data analytics, automation, and data‑driven techniques to enhance audit coverage, efficiency, and assurance quality. The role focuses on extracting, validating, analysing, and visualising large datasets to identify control weaknesses, exceptions, emerging risks, and potential fraud, while enabling more effective and continuous auditing practices.This role requires strong data analytics capability with working knowledge of data engineering principles, while remaining firmly rooted within an Internal Audit and assurance context.The Data Auditor works closely with Internal Auditors, IT, and business stakeholders to ensure audit conclusions are supported by reliable data and robust analytical procedures.Key ResponsibilitiesData extraction, quality and governance: Extract audit‑relevant data from core systems and data warehouses; assess and document data accuracy, completeness, and limitations; review SQL and data logic; and provide guidance on data access appropriateness from a governance perspective.Audit analytics and testing: Perform rule‑based and scenario‑based testing across full data populations to identify control failures, exceptions, and risks; support auditors with hands‑on data analysis; and assist in defining audit populations, samples, and testing approaches.Automation: Develop and enhance audit automation; build and maintain automated audit tests and recurring analytics; and identify opportunities to replace manual audit procedures with data‑driven approaches.Visualisation and reporting: Develop dashboards and analytical reporting to present audit results clearly; translate complex data outputs into concise insights for audit reporting and stakeholder communication.Data engineering awareness and data integrity: Assess data pipelines, transformation rules, and data models supporting audit analytics; identify data integrity, completeness, and logic risks arising from ETL processes and system integrations.Capability enablement and collaboration: Enable Internal Audit’s use of data tools and analytics; support development of self‑service audit data products; and collaborate with IT and business teams to understand systems, data flows, and changes impacting audit analytics.Key Attributes for Success in Internal AuditStrong attention to detail and professional scepticism.Ability to link data anomalies to audit risks and internal control implications.Methodical, well‑documented approach to ensure audit work is repeatable and defensible.Collaborative mindset with a service orientation toward the Internal Audit team.Ability to think “upstream” about how data is created, transformed, and governed, not only how it is analysed.