Python Trading Systems Developer (Front Office Equities) – Production Engineering & SRE
Design and build production-grade Python systems for global equities trading workflows. Own core components of order lifecycle, execution pathways, and market data processing. Requires 3-5 years of high-performance Python engineering with strong production ownership.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
Python Trading Systems Developer (SRE / Production Engineering) – Front Office Equities
Location: New York, USA (On-site)
Compensation: $300,000 – $600,000 Total Compensation + Significant Upside
Visa Sponsorship & Relocation Support Available
We are partnering with a leading global hedge fund building and scaling its Front Office equities trading technology stack. This is a high-impact engineering role focused on the development and production ownership of real-time trading systems powering execution, order management, and market data across global equity markets.
This role sits directly within the Front Office technology group. It is designed for strong Python engineers who build production systems at scale and want ownership of live trading infrastructure where correctness, latency, and reliability directly impact trading performance.
This is a trading systems engineering role with embedded production ownership, not support, not infrastructure maintenance, and not a pure SRE function.
The Role
You will join a high-performing Front Office engineering team responsible for building and operating a global equities trading platform spanning OMS/EMS, execution workflows, market data distribution, and post-trade processing.The environment is engineering-led, performance-sensitive, and highly collaborative with trading and quantitative teams. You will be expected to contribute as a hands-on Python engineer while also owning production stability of core systems.
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You are expected to operate as a systems builder first, production owner second.
Key Responsibilities
- Design and build production-grade Python systems powering equities trading workflows
- Own core components of the order lifecycle, execution pathways, and market data processing
- Develop robust, scalable services and tooling used directly in live trading environments
- Improve reliability, correctness, and performance of distributed trading systems
- Build observability and diagnostic tooling across critical production infrastructure
- Investigate and resolve complex production issues under time-sensitive trading conditions
- Automate operational workflows to eliminate manual intervention in production systems
- Contribute to architecture and design of high-throughput, low-latency trading components
- Partner directly with traders, portfolio managers, and quants to refine system behaviour
- Participate in production releases, validation, and real-time monitoring of trading systems
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Required Experience
- 3–5 years’ experience as a Python software engineer, ideally in high-performance or real-time systems
- Strong production-level Python development experience (services, distributed systems, or core backend systems)
- Demonstrated ability to build and maintain complex systems, not just scripts or tooling
- Experience working with real-time, distributed, or event-driven architectures
- Strong SQL and data interrogation skills in production contexts
- Strong Linux/Unix engineering capability
- Proven ability to debug and resolve deeply technical production issues under pressure
- Experience working directly with trading, quantitative, or similarly high-stakes business users
- Strong engineering fundamentals: correctness, performance, and system design thinking
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Preferred Experience
- Experience in equities trading systems, electronic execution, or market data infrastructure
- Exposure to OMS/EMS platforms, execution systems, or order routing logic
- Understanding of equity market structure and real-time trading dynamics
- Experience building production Python services (APIs, event-driven systems, distributed services)
- Familiarity with observability systems in high-availability environments
- Exposure to post-trade processing, reconciliation, or risk infrastructure
- Experience in environments where system latency, reliability, and correctness are tightly coupled to business outcomes
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