Description

• Lead end-to-end business analysis initiatives across complex, cross-functional projects, from discovery through delivery and post-implementation evaluation.
• Act as a domain expert in customer risk, fraud prevention, and underwriting (BNPL / Consumer Finance), proactively identifying opportunities for optimization and innovation.
• Partner with product leadership, engineering, data science, and stakeholders to shape product strategy and influence roadmap decisions.
• Drive the decomposition of large initiatives into epics and user stories, ensuring clarity, scalability, and alignment with architectural principles.
• Own and refine business and system requirements, ensuring consistency across systems (e.g., workflow engines, decision systems, data platforms).
• Assess the impact of regulatory, risk, and fraud-related changes (e.g., BaFin or EU requirements) on systems and processes.
• Ensure high-quality delivery by defining acceptance criteria, supporting testing strategies, and validating complex business logic.
• Mentor and support junior analysts, promoting best practices in documentation, analysis, and stakeholder communication.
• Contribute to continuous improvement of analysis frameworks, tooling, and team ways of working.

• Degree in Business Informatics, Computer Science, Economics, or a comparable field.
• 5+ years of experience as a Technical Business Analyst (or similar role) in FinTech, payments, or financial services.
• Strong domain expertise in risk, fraud, or payment processing systems.
• Proven experience leading complex initiatives and influencing cross-functional stakeholders.
• Advanced ability to translate business needs into scalable system designs, including APIs, workflows, and data models.
• Deep experience working in agile environments (Scrum/Kanban), including backlog ownership and refinement.
• Excellent analytical thinking, problem-solving, and stakeholder management skills.
• Strong communication skills, with the ability to engage both technical and non-technical audiences.
• High level of ownership, autonomy, and decision-making capability.
• Fluency in English. German language skills are a strong plus.

• Experience with BNPL, consumer finance, or credit risk decisioning systems.
• Hands-on experience with data analysis tools (e.g., Python, SQL, Databricks, Power BI).
• Familiarity with fraud detection systems and rule/ML-based decision engines.
• Exposure to regulatory requirements in banking and financial services (e.g., BaFin, PSD2, GDPR).