Fraud & Forensics Data Engineering
Ovations Technologies · Johannesburg, Gauteng
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Start free — we apply for you →- We are looking for an experienced senior: Fraud & Forensics Data Engineer to join a leading organisation and deliver data engineering solutions that support fraud detection, forensic investigations and financial crime analytics across multiple markets.
- This is a hands-on technical role requiring strong data engineering skills combined with an understanding of fraud and financial crime environments.
Key Responsibilities
- Design, develop, test, deploy and support fraud and financial crime data solutions.
- Translate fraud problem statements and investigation requirements into practical technical solutions.
- Design and develop ETL/ELT pipelines, data models, analytical datasets and data products.
- Implement batch, streaming and event-driven data ingestion solutions.
- Develop fraud-specific data marts, dimensional models, star schemas and analytical views.
- Build reusable data products supporting transaction monitoring, suspicious activity, customer behaviour, merchant/agent patterns and forensic investigations.
- Develop analytical datasets and reporting layers for fraud dashboards, alerts, scorecards and trend analysis.
- Implement data quality checks, validation rules, reconciliation logic and exception handling.
- Troubleshoot data pipeline failures, transformation issues, latency and data-quality problems.
- Support fraud investigation teams with data extracts, investigation packs and pattern-analysis datasets.
- Automate recurring data preparation, monitoring, validation and reporting processes.
- Ensure solutions comply with data governance, security, privacy, audit and regulatory requirements.
- Work closely with Fraud & Forensics, Data, Technology, Risk, Compliance and Information Security teams.
- Provide technical documentation, knowledge transfer and post-deployment support.
Minimum Requirements
- Bachelor's degree in Computer Science, Information Systems, Data Engineering or a related field.
- 5–7+ years' experience in Data Engineering, Data Modelling or Data Architecture.
- Strong hands-on experience with:
- SQL
- Python
- Relational databases such as SQL Server, PostgreSQL or Oracle
- Data modelling across conceptual, logical and physical levels
- Dimensional modelling / Star Schema
- ETL / ELT and data pipeline development
- Experience with cloud data platforms such as:
- Azure Synapse
- Azure Data Factory
- Databricks
- Snowflake
- BigQuery
- Understanding of data warehousing, big-data concepts, data governance, data quality and security.
- Familiarity with Power BI or other BI/reporting tools.
Fraud & Financial Crime Experience
The ideal candidate should have knowledge or experience in areas such as:
- Fraud detection systems and engines
- Fraud rules and alert logic
- Transaction monitoring
- Financial crime and forensic investigations
- Fraud typologies and risk indicators
- Suspicious behaviour/pattern detection
- Case management workflows
- Fraud data requirements and feature definition
- False-positive management and rule tuning
- Digital payments, wallet and mobile money fraud
- Fraud system testing and business validation
- Risk controls, governance and auditability
Advantageous
- Certification or exposure in Fraud Examination, Financial Crime, Data Science, Analytics, Risk Management or Model Governance will be advantageous.
Ideal Candidate
- We are looking for someone who combines strong hands-on data engineering capability with fraud-domain knowledge. You should be analytical, investigative, detail-oriented and comfortable working with both technical and fraud/forensics stakeholders.