Job Opportunities API

The Public Ledger of Openings

← Back to the ledger

Data Engineer, Fraud

Xcelirate
CompanyXcelirate
CategoryUncategorised
LocationBerlin
RemoteRemote
EmploymentFull-time
LevelNot stated
SalaryNot stated by the employer
Posted8 Jun 2026
Last verified3 Aug 2026
SourceEmployer career page (workable)
Applications are handled by the employer, not by us.Apply on the employer's site →
Description
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
HOUSE AD1,952,725 openings. Erioun finds yours.Scored against your own profile, every hour.Try the radar →