Fraud & Forensics Data Engineering at Ovations Technologies

• 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.