Engineer - Scalable Quantitative Computing & Data Infrastructure

Keywords:Software Engineer, Python, Fixed Income Analytics, Cloud Platforms, CI/CD pipelines, ML and AI financial applications

We are seeking a senior engineer with hands-on experience building large-scale computing platforms, data infrastructure, and analytics systems. The strongest candidates will have worked on technology designed to process large volumes of data and computationally intensive analytical workloads across multiple users, models, and applications., * 5+ years building large-scale computing, data, or analytics platforms. \n

• Demonstrated experience with distributed computing, parallel processing, high-performance computing, or large-scale data processing.

n \n

• Architect and build scalable computing and analytics infrastructure for computationally intensive quantitative workloads. \n

• Design distributed and parallel computing frameworks capable of substantially increasing processing capacity and speed. \n

• Build scalable data pipelines, data services, APIs, and analytical infrastructure supporting multiple models and applications. \n

• Develop reusable computational libraries and services used across quantitative development teams. \n

• Leverage cloud computing, distributed systems, Kubernetes, containers, and modern data-engineering technologies. \n

• Improve performance, scalability, reliability, deployment, testing, and monitoring of a large quantitative platform. \n

• Partner with quantitative researchers and investment professionals to translate complex analytical requirements into scalable production systems. \n

\n

\n, * Strong background in data engineering and analytics infrastructure, including pipelines, data services, and high-volume processing. \n

• Experience architecting shared platforms used by multiple developers, models, or applications. \n

• Strong Python and/or C++ engineering skills. \n

• Experience with cloud infrastructure such as GCP, AWS, or Azure. \n

• Experience with technologies such as Kubernetes, Docker, CI/CD, distributed processing, workflow orchestration, and automated testing. \n

\n

\n

Highly Desirable

\n

Experience building infrastructure for quantitative finance, fixed income, structured credit, pricing, valuation, risk, scientific computing, or other computationally intensive environments.

\n

The ideal candidate combines scalable systems engineering, data engineering, and quantitative computing and has built infrastructure that materially increased an organization’s analytical capacity and speed.

\n

\n

A leading New York-based structured credit investment firm is building a next-generation scalable computing, data, and analytics platform to significantly increase the speed and capacity of its quantitative investment processes.