In this hands-on role, you will build and operate the end-to-end data workflows that power loan-level and structured credit modeling.

Design and maintain large-scale data pipelines for credit, mortgage, and structured product analytics.
Develop and optimize loan-level feature engineering workflows and model input datasets.
Create reproducible data processing frameworks that support research, validation, and production deployment.
Collaborate with quantitative researchers to implement new features, validate methodologies, and improve model performance.
Partner with engineering teams to productionize research outputs and enhance platform scalability and reliability.
Support ad hoc quantitative analysis using portfolio, collateral, and performance datasets.

Strong Python experience with production-quality code, including testing, packaging, and code review practices.
Deep expertise in distributed data processing with Spark and PySpark, including optimization of joins, partitioning, caching, skew management, and execution performance.
Advanced SQL skills, including querying large columnar data warehouses such as Snowflake, Redshift, BigQuery, Vertica, or similar.
Experience building analytical datasets and feature engineering workflows for machine learning, statistical modeling, or quantitative research.
Strong understanding of reproducible data pipelines, experiment tracking, artifact management, and version-controlled development.
Experience working in shared engineering environments using Git, automated testing, and CI/CD processes.
Ability to work directly with quantitative researchers and translate research requirements into scalable engineering solutions.

Experience with loan-level, mortgage, consumer credit, or structured finance datasets.
Exposure to prepayment, default, transition, or loss modeling in credit or securitized products.
Familiarity with market/reference data providers, securitization cash flows, collateral reporting, or structured product analytics.
Experience with Databricks, Delta Lake, workflow orchestration, and modern cloud-based analytics platforms.
Exposure to model deployment, scoring frameworks, experiment tracking, or machine learning operations.
Experience with high-performance analytics tools such as Polars, DuckDB, Pandas, and scikit-learn.
Familiarity with workflow scheduling, data quality monitoring, and pipeline validation.
Comfort using AI-assisted development tools to accelerate coding, refactoring, testing, and codebase navigation.
Knowledge of cloud infrastructure, object storage, access controls, and cost-efficient data architecture.

Bachelor's, Master's, or PhD in Computer Science, Data Science, Statistics, Financial Engineering, Mathematics, or Economic

Location: New York, NY (onsite)

Compensation: USD 350,000 - 450,000 per yearly

Quantitative Data Engineer - Fixed Income and Mortgages

Job Description