Quantitative Data Engineer - Fixed Income and Mortgages

This hands-on Quantitative Data Engineering role supports end-to-end data workflows for loan-level and structured credit modeling, with a focus on building scalable pipelines and production-ready datasets used in quantitative investment and risk analytics.

Responsibilities

• 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 and investigate portfolio, collateral, and performance datasets.

Requirements

• 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 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 working in shared engineering environments using Git, automated testing, and CI/CD processes.
• Ability to work directly with quantitative researchers and convert research requirements into scalable engineering solutions.

Technologies

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

Preferred Qualifications

• 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 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.
• Comfortable 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.

Education

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

Location and Compensation

Location: New York, NY (onsite)

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