We're looking for a Data Engineer (Fraud Analytics & Intelligence) to join our growing team and help shape the data foundations behind our fraud detection, document intelligence and AI platforms. This isn't a traditional data engineering role focused solely on pipelines and reporting. You'll work with large-scale datasets, data lakes, fraud intelligence data and analytical workflows that directly support how we detect suspicious behaviour, uncover patterns and improve decision-making.

You'll partner closely with engineering, product and data teams to ensure our data is reliable, accessible and ready to power the next generation of Fortiro's fraud and intelligence capabilities.

If you're naturally curious, enjoy solving complex data problems and want your work to have a real-world impact, we'd love to hear from you.

What you’ll be doing

• Build and maintain the data foundations that power Fortiro's fraud detection, document intelligence and AI platforms.
• Develop Python and SQL workflows to ingest, transform and validate large-scale datasets.
• Manage and optimise data within our AWS data lake environment, including S3, Glue and Athena.
• Improve data quality, monitoring and reliability across critical data pipelines.
• Analyse complex datasets to uncover patterns, anomalies and actionable insights.
• Prepare high-quality datasets that support analytics, machine learning and fraud intelligence initiatives.
• Collaborate with engineers, product managers and data specialists to deliver scalable, trusted data solutions.
• 
  

What you'll bring to the role

• Strong Python fundamentals, including functions, data structures, file handling and common data libraries.
• Hands-on experience with pandas or similar tools for cleaning, transforming and analysing data
• Strong SQL skills and experience working with large, complex datasets
• Experience building or supporting data engineering workflows and ETL/ELT processes
• Strong understanding of data quality, validation and troubleshooting
• Ability to work with sensitive customer, financial or operational datasets
• Excellent analytical and problem-solving skills
• Strong documentation and communication skills
• 
  

Nice to Have

• AWS and cloud-based data platforms, including data lakes and large-scale analytical datasets
• Fraud, risk, fintech, financial services or other data-intensive SaaS environments
• Graph databases, anomaly detection or pattern-based analytics
• Machine learning and AI data workflows
• Modern engineering practices such as CI/CD, infrastructure-as-code and observability