At ThetaRay, our purpose is to make the world a safer place by protecting the integrity of the global financial system.

We do this by putting AI at the core of both our technology and our way of working. Our AI-driven solutions help banks and fintech companies worldwide detect and stop serious financial crime, from human trafficking and terrorist financing to sophisticated money laundering, while advanced technology, automation, and AI-driven tools help our teams collaborate smarter, move faster, and continuously improve how we build, deliver, and innovate.

About the role:
We are looking for a French speaking Data Engineer to turn expertise, initiative, and bold thinking into real impact on the next generation of AI-driven financial crime detection.

If you combine strong data engineering capabilities with hands-on experience in building and optimizing data pipelines and transformations at scale, and if you are motivated by designing the data flows that power real-world money laundering detection for general financial institutions, ThetaRay could be your next challenge.





Responsibilities:
- Implement and maintain data pipeline flows in production within the ThetaRay system based on the data scientist’s design
- Design and implement solution-based data flows for specific use cases, enabling the applicability of implementations within the ThetaRay product
- Building a Machine Learning data pipeline
- Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader
- Work with product, R&D;, data, and analytics experts to strive for greater functionality in our systems
- Train customer data scientists and engineers to maintain and amend data pipelines within the product
- Travel to customer locations both domestically and abroad
- Build and manage technical relationships with customers and partners
- 2+ years of Hands-on experience working with Apache Spark - must
- Hands-on exper

📌 Data Engineer (Madrid)
🏢 Thetaray
📍 Madrid