Fraud Data Analyst: Real-Time Risk Engineer (Hybrid, Unlimited PTO)

Satispay; Barcelona; Remote; EUR 55,000 - 75,000; Full time

Benefits offered by this job

Health insurance

Stock options

Meal vouchers

Relocation support

Professional development

Job summary

Satispay is seeking a Fraud Data Analyst to secure the future of digital payments by building real-time defense strategies. You will architect a fast decisioning engine and translate fraud patterns into high‑performing rules, while collaborating with data science to operationalize ML models and maintain a user-friendly security experience.

Ideal candidates have 3–7 years in risk/payments, strong SQL/Python skills, and fluency in English.

Qualifications

• 3–7 years of professional experience in risk, payments, or credit sectors.
• Strong SQL and Python for data analysis and strategy development.
• Solid understanding of payment fraud dynamics and real‑time decisioning environments.

Responsibilities

• Architect Real‑Time Strategies by designing a fast, rule-based and ML‑assisted decisioning engine.
• Develop precision rules to balance false positives and fraud loss.
• Deconstruct payment fraud by identifying new schemes and creating end‑to‑end mitigations.
• Collaborate with product and engineering to define analytical requirements and data signals.
• Calibrate ML models with heuristic rules to optimize performance and user experience.
• Monitor rule efficacy and report performance to leadership.

Skills

SQL

Python

Risk & Fraud Domain

Real-Time Decisioning

Communication

Tools

PySpark

GraphQL

Job description

Satispay is seeking a Fraud Data Analyst to secure the future of digital payments by building real-time defense strategies. You will architect a fast decisioning engine and translate fraud patterns into high‑performing rules, while collaborating with data science to operationalize ML models and maintain a user-friendly security experience.

Ideal candidates have 3–7 years in risk/payments, strong SQL/Python skills, and fluency in English.