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ML Data Engineer - Mortgage Credit Risk - Anza Mortgage Insurance Company
Data Engineer
Lead the design and implementation of data pipelines and ML models to assess mortgage credit risk, leveraging Python, SQL, and AWS services.
About the role
Key Responsibilities
- Design, develop, and maintain scalable data pipelines that ingest, transform, and store mortgage-related data from multiple sources.
- Build and deploy machine learning models to predict credit risk, ensuring model accuracy, fairness, and compliance with regulatory standards.
- Collaborate with data scientists, underwriting teams, and product managers to translate business requirements into technical solutions.
- Implement monitoring, logging, and alerting for data quality and model performance in an AWS environment.
- Document data schemas, pipeline logic, and model artifacts to support auditability and knowledge transfer.
Requirements
- 3+ years of experience as a data engineer or ML engineer in a financial services context.
- Proficiency in Python, SQL, and experience with data orchestration tools (e.g., Airflow, Prefect).
- Hands‑on experience building and deploying ML models using frameworks such as scikit‑learn, TensorFlow, or PyTorch.
- Strong knowledge of AWS services (S3, Redshift, SageMaker, Glue, Lambda).
- Excellent problem‑solving skills and ability to work in a fast‑paced, cross‑functional team.
Skills
pythonsqlmachine learningaws