Credit Risk Manager (Mortgage)

About the opportunity

N26 is looking for a Credit Risk Senior Associate to join N26's Retail Mortgage team. The core purpose is to design, implement, and manage robust data analytics and second-line process controls for the mortgage portfolio. The role emphasizes deep methodological expertise with data processing tools (SQL, Python, R) and a proactive curiosity for broader mortgage business aspects, including macroeconomic analysis of collateral and digital process controls. The Senior Associate will ensure IFRS 9 models, early warning and control frameworks are implemented in strict compliance with regulatory requirements.

In this role you will

• Contribute significantly to the further development of N26's mortgage credit risk function, aligning with N26's growth aspirations, robust risk management objectives, and stringent regulatory compliance requirements within the retail mortgage business.
• Play a key role in the continuous enhancement of N26's global mortgage governance framework, policies, and procedures, ensuring full alignment with the latest Dutch (e.g., THRK), European (e.g., EBA GL LOM), and German (MaRisk, KWG) regulations.
• Support the further development and implementation of controls for the end-to-end mortgage credit processes. This includes, but is not limited to, loan granting, comprehensive collateral management, sophisticated monitoring, proactive early warning systems, intensified and problem loan management, and accurate loan loss provisioning.
• Design, implement, and operate robust second-line control frameworks and tools for mortgage processes and the portfolio, including for the oversight of business activities and effective monitoring of third-party service providers.
• Develop and maintain a resilient and accurate mortgage credit risk database, crucial for precise portfolio and single case monitoring, portfolio reporting, and loan loss provisioning, with a strong focus on data quality and integrity.
• Conduct in-depth data analysis and support further development and implementation of IFRS 9 (PD, LGD) and quantitative models to assess key risk indicators across obligor, collateral, and portfolio levels within the mortgage domain.
• Proactively run comprehensive risk analyses, including assessing the impact of macroeconomic factors on collateral values and broader portfolio performance.
• Partner with Mortgage Business, Group Treasury, Capital Markets, Corporate Finance, Risk Controlling, Accounting, Regulatory Reporting, and Tech departments to implement credit requirements, help ensure transparency, and compliance with applicable credit law and regulations, and contribute to establishing a risk culture in the organization.

Background

• Bachelor's degree in finance, econometrics, statistics, mathematics, computer science, or a comparable quantitative field; an additional qualification (e.g., Master's degree in a relevant field, passed CFA or FRM exams) is a strong plus.
• 3+ years of experience in credit risk management (experience with retail mortgages is an advantage), data analytics, process controls, or credit risk methodology development. Previous experience in a FinTech environment is a plus.

Skills

• Proven experience in building, implementing, and running credit process controls and oversight frameworks.
• Good understanding of key risk indicators across obligor, collateral, and portfolio levels within the mortgage domain.
• Solid understanding of processes across the entire value chain of mortgage or similar credit products (e.g., credit decisioning, monitoring, early warning, intensified and problem loan management).
• Solid knowledge of European regulations (e.g. EBA), exposure to Dutch mortgage regulations (e.g., THRK) or German (MaRisk, KWG) regulations being advantageous; successful track record of understanding their implementation and liaising with supervisory authorities is a strong plus.
• Strong proficiency in SQL for data extraction, manipulation, and analysis; experience with other programming languages (like Python or R, or other data processing tools) for quantitative analysis and model development is a significant advantage.
• Solid knowledge of credit risk methodology, including PD/LGD models, debt-servicing ability, and advanced collateral valuation techniques.