Quantitative Platform Engineer

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🕒 May 14

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SilverTide

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

💳 Fintech

💸 Finance

☁️ SaaS

Fintech • Finance • SaaS

SilverTide is a leading fintech specializing in buy-side brokerage services, focused on providing advanced software solutions for portfolio managers. The company leverages cutting-edge technology to offer differentiated trade ideation, optimization, and execution services, particularly in interest rate derivatives for major hedge funds located in financial hubs like New York, London, Dubai, and Geneva. With a strong emphasis on data-driven decision-making and a client-first approach, SilverTide aims to revolutionize investing through quantitative analytics.

📋 Description

• Architect and develop components of our core analytics platform, including data pipelines, pricing services, and API layers consumed by research application. • Design and implement scalable data ingestion and processing systems for financial and macroeconomic data - with a focus on reliability, latency, and coverage. • Build and maintain internal tooling and libraries that standardize how models, analytics, and trade signals are developed, tested, and deployed across the team. • Own the software lifecycle of platform components: design, code review, testing, monitoring, and incremental improvement. • Develop trade discovery and screening tools, ensuring they are performant, maintainable, and extensible. • Collaborate with quants to translate analytical requirements into well engineered platform features - acting as the bridge between research and production. • Continuously improve platform observability, deployment practices, and developer experience.

🎯 Requirements

• Bachelor's or Master's in Computer Science, Software Engineering, or a related technical discipline. • Strong software engineering fundamentals: system design, clean architecture, testing, and code quality. • 3+ years of production experience in a platform, quant development, or data engineering role within financial services. • Expert-level Python; familiarity with C++ or Rust is a plus for performance-critical components. • Hands-on experience building and maintaining data infrastructure - time-series databases, data pipelines, and API services. • Experience with pricing library or quantitative model implementation, ideally in a production rather than purely research context. • Strong instincts around performance, reliability, and operational concerns in fast-moving trading environments. • Clear communicator who can work effectively across trading, research, and engineering functions.