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$160,937–$222,000.
Based on 240 similar postings.
Employer
About Santander
Santander is one of the world''s largest banks headquartered in Spain, providing retail, commercial, and investment banking services to individuals, small businesses, and large corporations in Europe, the Americas, and beyond. Industry: Banking & Financial Services
The Senior Financial Risk Analytics Engineer joins the team to support critical banking activities including reserving, stress-testing, underwriting, and financial forecasting. This role involves designing, developing, and maintaining data workflows, analytical solutions, and credit loss models to ensure regulatory compliance and informed risk management. The candidate will translate complex business requirements into scalable technical solutions while managing CECL credit loss models and creating automated processes for reporting and performance monitoring. Key technologies and skills include SQL, Oracle, SAS, R, Python, and cloud platforms like AWS, Azure, or GCP. Additionally, the role requires expertise in data orchestration tools such as Apache Airflow, visualization tools like Tableau or Qlik Sense, and experience with CI/CD pipelines. The work focuses on solving complex financial risk problems within a banking environment through advanced statistical modeling and robust data engineering practices.
Design, develop, and maintain credit loss models for CECL, stress-testing, reserving, and underwriting activities.
Build and maintain robust data workflows and automated processes to support forecasting and risk management.
Translate complex business requirements into scalable analytical solutions and technical infrastructure.
Create user-friendly reports, dashboards, and data visualizations to monitor performance and support decision-making.
Resolve data quality, workflow, and reporting challenges across various internal systems.
Maintain comprehensive model documentation, workpapers, and change management records for audit and regulatory compliance.
Author technical reports documenting model design, testing methodologies, and ongoing enhancements.
Manage the lifecycle of analytical projects including research, scoping, and implementation planning.
What we're looking for
Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, Mathematics, Statistics, Finance, Economics, or a related quantitative field.
Master's degree in a quantitative discipline (preferred).
10+ years of experience working with complex relational database management systems including Oracle, SQL, or similar platforms.
10+ years of software engineering, data engineering, or analytics experience within a commercial bank or financial institution.
5+ years of modeling, quantitative analytics, or financial analytics experience within a banking or financial services environment.
Experience designing, developing, and maintaining large-scale data workflows and automated data pipelines.
Experience supporting reserving, stress-testing, underwriting, forecasting, or risk management solutions.
Python
AWS
Machine Learning
NLP
Generative AI
H2O
Spark
Conda
SQL
Scala
R
Deep Learning
Time Series
Sentiment Analysis
Clustering
Classification
Big Data
Statistical Modeling