Data Engineer (Audit)
Summary
Build and maintain ETL pipelines to process large datasets, then use Python/R, SQL, and analytics to detect fraud and policy violations across financial systems.
Responsibilities
- Developing and maintaining data pipelines for efficient data extraction, transformation, and loading (ETL) processes.
- Proactively detect indications of fraud, collusion or policy violations in structured and unstructured data, using advanced analytics (predictive, prescriptive) to test 100% of the data population, to identify anomalies, patterns, and risk trends.
- Translating Complex Data Findings into Actionable Insights
- Identify, analyze, and prioritize IT risks, root causes, business impacts, and control gaps across cybersecurity, data protection, system availability.
- Deliver practical, risk-based, and business-oriented audit reports with clear recommendations to strengthen controls, improve operational resilience, and support sustainable business improvement.
- Monitor, validate, and report the status of audit issue remediation to ensure management action plans are effectively implemented and evidence-based.
Qualifications
- Minimum 3 years of experience in technical designing and deploying large-scale distributed data processing systems and communicate insights effectively.
- Solid knowledge framework and systematic thinking, familiar with Data Mining, Data Modeling, DAX (Data Analysis Expressions) and Big Data
- Proficient in programming languages for data analysis (Python and/or R) and advanced SQL (GoogleSQL / MySQL / PostgreSQL) for data querying and manipulation.
- Have basic knowledge of data processing services GCP (BigQuery,DataFLow) / AWS (Redshift, EMR) will be an advantage
- Have basic knowledge of enterprise systems (SAP / Odoo / Accurate, etc..) will be an advantage