Risk Analytics, Audit Analytics

About the Role

As a Senior – Audit Analytics, you will support the delivery of data analytics solutions across Internal Audit, risk and controls engagements. You will work with large and complex enterprise datasets to develop, validate and visualize analytics that help identify risk indicators, control exceptions and process improvement opportunities. The role requires strong hands-on analytical capabilities and the ability to translate audit and business requirements into scalable analytical solutions using technologies such as SQL, Python, Power BI, ETL tools and modern data platforms including Databricks and Snowflake. You will also have opportunities to contribute to technology-enabled Internal Audit transformation, including automation, Continuous Controls Monitoring (CCM), and AI/GenAI-enabled analytics.

Key Responsibilities

• Perform data extraction, transformation, profiling and analysis using SQL, Python and ETL tools/techniques.
• Develop and execute audit analytics, exception testing and risk-based analytical procedures across enterprise processes.
• Build interactive dashboards and reporting solutions using Power BI to communicate analytical findings, exceptions and risk insights.
• Work with large and complex datasets across modern data platforms such as Databricks and/or Snowflake.
• Perform data quality checks, reconciliations and validation to ensure the completeness and accuracy of analytical outputs.
• Translate audit, risk and business requirements into analytical logic, data requirements and test scenarios.
• Collaborate with Internal Audit, business, technology and data teams to understand processes, identify relevant data sources and deliver analytics.
• Document data logic, transformation steps, assumptions and analytical procedures to support repeatability and auditability.
• Support technology-enabled Internal Audit transformation initiatives, including analytics, automation and Continuous Controls Monitoring.
• Identify opportunities to leverage AI/GenAI and Large Language Models (LLMs) within analytics, audit and risk management use cases.

Requirements & Tech Stack

• Strong hands-on experience with SQL, including complex queries, joins, aggregations, data profiling and analytical logic.
• Strong hands-on experience with Python for data analysis, transformation, statistical modelling, automation and development of scalable analytical solutions.
• Strong understanding of ETL concepts, data transformation and working with data from multiple source systems.
• Hands-on experience developing dashboards and analytical reporting solutions using Power BI.
• Experience working with data warehousing and modern data platforms such as Databricks and/or Snowflake.
• Strong data analysis, reconciliation and problem-solving skills, with the ability to work effectively with large and complex datasets.
• Ability to understand business, audit and risk requirements and translate them into structured analytical tests and actionable outputs.
• Strong communication and documentation skills, with the ability to explain analytical approaches and findings to business and audit stakeholders.
• Exposure to AI/GenAI solutions and Large Language Models (LLMs) and their application across analytics, audit and risk management.
• Ability to work collaboratively across multidisciplinary teams and manage assigned analytical deliverables.
• Understand common business and technology processes such as Procure-to-Pay (P2P/PTP), Order-to-Cash (O2C/OTC), Travel & Expense (T&E), ITGC and SOX controls.
• Have an understanding of risks, controls, KRIs and exception-based audit testing.
• Have exposure to enterprise systems and datasets such as SAP, Oracle, Workday, expense platforms, GRC tools or other ERP environments.
• Have experience or exposure to Internal Audit transformation initiatives involving analytics, automation or technology-enabled auditing.

Qualifications

• Hands-on analytics professional who combines strong technical capabilities with an understanding of risk, controls and Internal Audit.
• Comfortable working with complex enterprise datasets, developing analytical solutions independently and translating technical findings into meaningful risk and business insights.
• Possess a problem-solving mindset, strong attention to data quality and the ability to collaborate effectively with audit, business, data and technology stakeholders.