Data Engineer, Fraud
Role summary by JobGrid
Data Engineer, Fraud at Xcelirate: Remote, France; Full time; Mid; IT; Data Engineer. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Remote, France
- Role classification: IT, Data Engineer, Full time, Mid
- Source freshness: checked by JobGrid on 2026-06-08.
- Application path: candidates continue to the employer application page with non-personal referral tags.
Who Are We?
Xcelirate develops technologically-advanced platforms which are accessed by thousands of users every minute! We are proud to offer a workplace where the sharpest developers come together to strategically plan and swiftly execute practices which see us maintain our existing market dominance and attain global expansion. We owe our success to our customers who have seen us grow across a decade, and our talented team who have made that growth possible.
Who are we looking for?
The Data Engineer will focus on designing, developing, and maintaining robust data infrastructure to support use cases such as fraud detection but also general data engineering. The position emphasizes building scalable, high-performing data pipelines and storage systems for fraud use cases. Although this role involves light integration with machine learning models, its primary responsibility is creating the technical foundation that powers such use cases of fraud detection, analytics and reporting.
What will you be doing?
- Develop and Maintain Pipelines: build and maintain efficient, scalable data pipelines for use cases such as fraud detection
- Support Fraud Analytics: enable analysts and product teams to identify and address emerging fraud patterns through engineered datasets
- Integrate Detection Models: collaborate with teams to operationalise external fraud detection models and integrate them into the data infrastructure
- Data Storage Optimisation: design and optimise data storage solutions for analysing fraud signals and managing historical data
- Feature Engineering: create fraud-specific datasets and features to enhance detection accuracy while supporting business and analytics teams
- Pipeline Monitoring and Optimisation: monitor fraud data pipelines to ensure system reliability and troubleshoot performance issues
- Best Practices Documentation: establish and document best practices for fraud-related data engineering
- Cross-Team Collaboration: partner with data, product, and engineering teams to proactively address fraud trends