C++ Market Data Engineer

Our client is a global financial institution operating at the intersection of technology and trading. This role involves working on a proprietary, high-performance, real-time market data distribution platform used globally across multiple trading desks.

• 6+ years of software engineering experience focused on real-time market data and distributed architectures.
• Expert-level proficiency in modern C++ (C++14/17/20) for high-performance, low-latency systems.
• Strong experience with direct exchange protocols (FIX/FAST, ITCH, OUCH) and vendor feeds.
• Deep understanding of network transport protocols (TCP, UDP, multicast) and messaging frameworks (Aeron, ZeroMQ, Kafka).
• Expertise in memory management, multi-threading/concurrency, CPU core affinity, and NUMA optimisation.
• Strong grasp of market data concepts, symbol mapping, segmentation, and A/B arbitration.
• Proficiency in Python for building APIs, integration, and data pipelines.
• Openness to AI-assisted development workflows.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

Nice to have:

• Experience or familiarity with Java.
• Experience with serialization formats like SBE, ProtoBuf, Avro, or FlatBuffers.
• Familiarity with containerisation and cloud-native tools (Docker, Kubernetes).
• Experience with real-time observability tools (e.g., Prometheus, Grafana).
• Domain knowledge in Equities, FX, Futures, or Commodities asset classes.

• Develop high-performance, low-latency market data distribution systems supporting large-scale access.
• Build and maintain direct exchange feed handlers (e.g., NYSE, NASDAQ, CME, Eurex) and vendor data integrations (e.g., Refinitiv, Bloomberg B-Pipe).
• Create robust data validation, quality assurance, and monitoring processes, including APIs and pipelines in Python.
• Collaborate with infrastructure, DevOps, and trading teams to ensure reliability, security, and scalability.
• Contribute to architectural decisions, troubleshoot production issues, and establish best practices for high-scale data access.

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