Senior Software Engineer

About Client

We are a fast-moving global firm operating in the crypto market-making space (not an exchange), but focused on building next-gen high-frequency trading infrastructure for market making in various global exchanges.

A Full Stack Developer at a High-Frequency Trading (HFT) firm builds high-performance internal dashboards, low-latency APIs, and real-time risk-management tools. The role requires heavy Python backend capabilities paired with frontend engineering, focusing entirely on speed, system reliability, and microsecond data flow.

Core Responsibilities

• Backend Development: Design ultra-low latency, multi-threaded Python microservices (using FastAPI or Flask) for order routing and risk assessment.
• Frontend Engineering: Build responsive, real-time trading dashboards and admin panels using React, TypeScript, and state management (e.g., Redux) to monitor live positions.
• Data Processing: Engineer WebSocket and WebRTC pipelines to handle real-time market data streaming without bottlenecks.
• Infrastructure: Implement containerized workflows using Docker and Kubernetes, alongside CI/CD deployment pipelines.
• Cross-Functional Support: Work directly with Quantitative Researchers and Algorithmic Traders to deploy and visualize trading strategies.

Essential Qualifications

• Experience: 3 to 6 years of hands-on Python experience, with a proven track record in algo/proprietary trading, HFT firms, or fintech.
• Programming: Deep expertise in core Python, Asynchronous programming, and JavaScript/TypeScript.
• Databases: Hands-on proficiency with time-series databases (e.g., TimescaleDB), caching tools (Redis), and relational databases (PostgreSQL).
• Low Latency: Strong grasp of memory profiling, OS-level concurrency (multi-threading/multi-processing), and network latency mitigation.

Preferred Qualifications

• Basic understanding of financial markets, order books, and exchange protocols (e.g., FIX, Binance API).
• Familiarity with C++ or Rust for performance-critical backend modules.
• Degree in Computer Science, Financial Engineering, or a highly quantitative STEM field.