Role Overview
Portfolio BI is hiring a Full Stack Quantitative Developer to design, build, and maintain end-to-end applications supporting credit, private credit, structured products, and CLO businesses. This hands-on engineering role involves writing production code, modeling financial cash flows, integrating market data, and modernizing analytics platforms by transitioning legacy systems to responsive, cloud-aware architectures.
Key Responsibilities
- Build full-stack applications including backend services, APIs, and modern web front ends for credit and structured products platforms.
- Develop quantitative models for fixed-income valuation, cash flow projections, and portfolio risk decomposition.
- Integrate third-party systems such as Geneva, market data vendors, and CRM platforms.
- Modernize legacy .NET/C# applications and SSRS reports into scalable architectures using TypeScript/React.
- Manage data quality, ingestion, normalization, and validation across the firm.
- Develop reporting and BI solutions including Tableau dashboards and internal web tools.
- Translate complex business requirements from PMs and analysts into technical specifications and production-ready code.
- Utilize AI coding assistants to accelerate development while maintaining strict code quality and security standards.
Requirements
Education
- Bachelor's degree or higher in computer science, mathematics, physics, financial engineering, or a quantitative discipline.
Experience
- 5+ years of professional software engineering experience with production ownership of business-critical systems.
- 2+ years in capital markets (hedge fund, asset manager, or fintech) with exposure to fixed income, structured products, or CLOs.
- Proven ability to deliver full-stack applications from requirements through deployment.
Technical Skills
- Languages: Strong proficiency in Python, C#/.NET, or TypeScript/JavaScript.
- Backend: REST APIs, microservice patterns, and asynchronous services.
- Frontend: React, Angular, HTML5, and CSS for responsive web design.
- Data: Expert-level SQL (window functions, query tuning) and NoSQL/document stores.
- Quant/Numerical: NumPy, pandas, basic statistics, and financial math (duration, convexity, OAS).
- Tooling/Cloud: Experience with Git, CI/CD, DevOps, and deployment on Azure or AWS.