Senior Data Engineer – Risk & Compliance (CNP Protect)

Berlin, Germany | Full-time | Office-first

Team description

The Card Not Present (CNP) Protect is a cross-functional team within the Risk & Compliance tribe that’s shaping SumUp’s global approach to fraud prevention. Our team is responsible for fraud prevention in our card-not-present products. We build cutting-edge systems and ML solutions for automated decision-making in the CNP space.

The Senior Data Engineer role is critical for creating a robust, high-quality data foundation that drives efficiency and enables data-driven decisions for the Risk & Compliance teams. You will build and operate the production data layer that powers automated decisioning for CNP fraud detection. You will own feature jobs and work closely with the ML Platform team to build and scale the pipelines. Our roadmap includes building infrastructure to generate and publish foundation model embeddings.

What you'll do

Design, build, and operate production-grade, Python-based data pipelines that power ML workflows, including real-time and near-real-time data processing and aggregations.

Own end-to-end data quality and reliability by implementing validations, automated testing, monitoring, and alerting — with clearly defined SLAs and ownership boundaries.

Strengthen and document our data foundations and governance, covering architecture, data lineage, dataset definitions, and dependency management.

Partner closely with the Risk Platform, ML Data Platform, and cross-functional teams — including data scientists, software engineers, and analysts — to align on requirements and deliver changes safely to production.

Drive Feature Store governance, improve usability, and standardize our Feature Store setup.

You'll be great for this role if…

4+ years of experience as a Data Engineer, focusing on building and deploying scalable data solutions.

Strong proficiency in Python and PySpark, with solid software development engineering skills for designing computationally effective solutions in large-