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.