Market Data Engineering Manager - C++
Job Description
[Up to c. $275k Base Salary + Discretionary Bonus (or equivalent) | Office-Led Working]
Role Overview
We’re representing a global multi-strategy investment firm building out the engineering leadership behind its core market data capability. The platform sits directly within the investment technology estate, providing both live and historical data to portfolio managers, traders, quantitative researchers and engineering teams across multiple asset classes.
This hire will take ownership of a specialist market data engineering team and play a major role in the architecture and development of the next generation platform. The scope runs from low-latency real-time ingestion and distribution through to historical data access, derived datasets and integration with research and trading workflows. This is deliberately a player-coach position. The successful individual will be an experienced engineering manager, but still technically credible enough to shape architecture, review complex C++ systems and get close to difficult performance and production problems...
Role Snapshot
- Bring substantial software engineering experience, typically 12+ years overall, including deep market data or low-latency trading systems expertise and meaningful engineering leadership
- Lead, develop and raise the technical bar of a small specialist team while remaining closely involved in architecture, design reviews, code quality and complex engineering decisions
- Own engineering across both real-time and historical market data, rather than specialising exclusively in streaming feeds or offline datasets
- Build and evolve high-performance C++ services handling market data ingestion, processing and distribution under demanding latency, throughput and reliability requirements
- Work with direct exchange and vendor data sources, with strong understanding of feed protocols, normalisation, symbology, data validation, arbitration and resilient processing
- Apply strong systems knowledge across TCP/IP, UDP multicast, concurrency, memory management, CPU utilisation, NUMA and practical performance optimisation
- Use Python to support APIs, integrations, data pipelines and tooling connecting the core platform with research, trading and analytical workflows
- Own production quality - including observability, incident response, root-cause analysis, capacity, recovery behaviour and long-term engineering improvements
- Partner directly with investment professionals and technology teams to translate requirements into platform architecture, priorities and production systems
- (Preferred) Experience with Bloomberg, Refinitiv, time-series technologies such as kdb+, or comparable enterprise market data and historical-data platforms
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