Quantitative Data Engineer - Fixed Income and Mortgages

This hands-on Quantitative Data Engineer role focuses on the end-to-end data workflow that supports loan-level and structured credit modeling. The position designs scalable pipelines and production-ready datasets used across quantitative investment and risk analytics.

Key Responsibilities

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

Required Qualifications

• Strong Python development experience, including production-quality code, testing, packaging, and code review practices.
• Deep experience with distributed data processing using Spark and PySpark, including optimization of joins, partitioning, caching, skew management, and execution performance.
• Advanced SQL skills, including experience querying large columnar data warehouses such as Snowflake, Redshift, BigQuery, Vertica, or similar platforms.
• 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 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.

Technologies

• Python
• Spark, PySpark
• SQL
• Snowflake, Redshift, BigQuery, Vertica
• Git
• CI/CD

Preferred Qualifications

• Experience working 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 and reference data providers, securitization cash flows, collateral reporting, or structured product analytics.
• Experience with Databricks, Delta Lake, workflow orchestration tools, 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.

Location

New York, NY (onsite)

Compensation

USD 350,000 - 450,000 per year

Education

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

Additional Notes

Candidates from adjacent industries are welcome, particularly those with strong distributed computing, data engineering, and machine learning experience.