The Function Purpose
The purpose of the Data Analyst is to support audit activities through the extraction, analysis, and visualization of data across the organization. The Data Analyst will play a key role in enhancing the department’s audit processes by identifying anomalies, trends, control weaknesses, and areas of risk using data-driven techniques. This role contributes significantly to risk assessments, audit planning, and the execution of data-enabled audits.
Main Responsibilities
- Collaborate with internal auditors to understand audit objectives and develop data analytics approaches to support audit planning, execution, and reporting.
- Extract, clean, transform, and analyze large datasets from multiple data sources including core banking systems, ERP systems, and operational platforms.
- Develop and execute automated scripts and queries (e.g., SQL, Python) to test controls, detect anomalies, and perform trend or risk-based analysis.
- Provide data-driven insights and visualizations through dashboards and reports using BI tools (e.g., Power BI, Tableau).
- Design and maintain reusable analytics models and dashboards to support continuous auditing and monitoring activities.
- Assist in the development of risk indicators and metrics to evaluate control effectiveness and operational performance.
- Support the audit team in identifying patterns, trends, and root causes of control gaps and irregularities.
- Document data sources, logic, and procedures used in audits to ensure transparency, repeatability, and compliance with audit standards.
- Stay updated on emerging data analytics trends and techniques relevant to internal audit and risk assessment.
- Contribute to building a data analytics capability within the department by mentoring auditors on the use of data analytics tools and methodologies.
Background
- Bachelor’s degree in computer science, Data Science or a related field.
- Minimum of 3–5 years of relevant experience in data analytics, preferably within internal audit.
- Data Analytics, Data Engineering, and Data Storytelling skills.
- Proficiency in data analytics tools and languages such as SQL, Python, R, or ACL.
- Proficiency in designing and developing ETL pipelines.
- Solid experience with business intelligence tools (e.g., Neteeza, Power BI, Tableau, QlikView, etc…).
- Big Data/Cloud, API, SAP HANA, and AI/Machine Learning experience is desirable.
- Advanced degree or certifications (e.g., CISA, CIA, CPA, Data Analytics certifications) are a plus.
- Experience working with audit or banking systems (e.g., Core Banking, ERP, Platforms) is highly desirable.
