Data Engineer, ML Engineer - Credit Decision Engine

Summary

Join our team as a Data Engineer/ML Engineer to set up lightweight infrastructure for a credit decision engine, design data pipelines, integrate open banking data, and deploy basic model infrastructure.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, or a related field
• 3+ years of experience as a Data Engineer, ML Engineer, or similar role, with expertise in creating simple, scalable data infrastructures
• Proficiency in cloud platforms (Google Cloud) and familiarity with model deployment tools
• Experience with building and managing data pipelines, especially for prototypes or MVPs
• Basic knowledge of security and compliance standards, particularly as they relate to financial data

Responsibilities

• Build Lightweight Data Pipelines: Develop essential data pipelines to ingest, clean, and process open banking data, focusing on simplicity and speed for rapid prototype testing
• Ensure data flows securely from source to model, with a focus on handling core data points such as income and transaction history
• Deploy Basic Model Infrastructure: Collaborate with credit expert to set up and test models within the prototype environment, providing a simple deployment pipeline for easy model updates
• Create basic APIs or microservices for model access, enabling smooth communication between the model and other system components
• Infrastructure Setup and Optimization: Set up a lightweight, cost-effective cloud infrastructure (using Google Cloud) that meets the minimal requirements for prototype testing
• Implement basic monitoring to track model performance and detect issues quickly, focusing on the flexibility needed for iterative testing
• Data Security and Compliance Basics: Ensure that data handling adheres to basic compliance and privacy standards, focusing on secure data transfer and storage
• Implement essential access controls and document processes for regulatory visibility, meeting minimum viable standards in alignment with SAMA guidelines
• Documentation and Collaboration: Document the architecture, data flows, and model integration processes, creating a clear reference for future development and iteration
• Work closely with the team to ensure the prototype meets initial functionality goals and supports rapid experimentation