Developing and maintaining data pipelines for efficient data extraction, transformation, and loading (ETL) processes.
Developing and implementation automation scripts (using Python, SQL) to improve the efficiency and scope of audit testing.
Developing and implementation audit procedures using advanced analytics to test 100% of the data population, to identify anomalies, patterns, and risk trends.
Proactively detect indications of fraud, collusion, or policy violations in structured and unstructured data
Translating Complex Data Findings into Actionable Insights
Perform ad-hoc assignment related to Continuous Audit, Operational Audit, Finance Audit, and IT Audit
Requirements:
Bachelor's degree in Computer Science, Data Science, equivalent
Minimum 4 years of experience in data analytics, audit analytics, data engineering, with hands-on exposure to data-driven or continuous audit practices.
Strong proficiency in Python and SQL for building ETL pipelines, automating audit testing, and performing large-scale data analysis with high data quality standards.
Solid understanding of audit, risk, and control concepts, with the ability to apply advanced analytics techniques to identify anomalies, patterns, trends, and potential fraud or policy violations across full data populations.
Strong analytical and communication skills, with the ability to translate complex data findings into clear, actionable insights and work effectively with audit, IT, and business stakeholders.
