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. 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 modeling, 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.
• Understanding of 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.
• Understanding of risks, controls, KRIs, and exception-based audit testing.
• Exposure to enterprise systems and datasets such as SAP, Oracle, Workday, expense platforms, GRC tools, or other ERP environments.
• Experience or exposure to Internal Audit transformation initiatives involving analytics, automation, or technology-enabled auditing.

Qualifications

• A 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.
• Possesses a problem-solving mindset, strong attention to data quality, and the ability to collaborate effectively with audit, business, data, and technology stakeholders.