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# C++ Software Engineer - Data Platform

- **Company:** WorldQuant
- **Location:** London, UK
- **Experience:** Experienced
- **Salary:** £59,188.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Code Review, Data Transmissions, Data Infrastructure, Data Structures, Shard (Database Architecture), Distributed Data Store, Distributed Systems, Memory Management, Hard Disk Drives, Python (Programming Language), Routing, Software Engineering, Data Streaming, Data Processing, Information Technology, C++14
- **Published:** July 27, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5816500365

## About the Role

* Degree in Computer Science or a related field.
 * 2+ years of professional software engineering experience.
 * Strong fundamentals in data structures, algorithms, and systems-level thinking (memory management, concurrency, I/O).
 * Proficiency in modern C++ (C++11 or higher); comfort with STL.
 * Solid working knowledge of UNIX/Linux development environments.
 * Experience using AI-assisted coding tools (e.g., Claude Code, OpenCode) to accelerate development.
 * A clear, proactive communicator who works well with a globally distributed team.

Nice to Have:

 * Python
 * Exposure to distributed systems concepts (replication, sharding, consensus), query/compute engines, or columnar/analytical storage formats.
 * Experience with RPC frameworks.

## Description

The Role: Our distributed platform combines high-performance storage, compute, and service layers for processing and serving massive, structured, time-series datasets - built in C++ with a distributed system architecture, real-time streaming, RPC-based data transfer, and columnar on-disk storage that powers data processing and analytics workflows.

 * Design, develop, and maintain core components across our distributed storage, compute, and service platforms - including storage engines, query and compute execution, RPC/networking layers, and distributed coordination (routing, partitioning, replication).
 * Partner with internal stakeholders across research and engineering teams to translate data-access, compute, and performance requirements into concrete design and implementation work.
 * Investigate and resolve production issues - from correctness bugs to performance regressions.
 * Participate in code review, system design, feature development, and productionization.


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