Java Developer | FX & Commodities Trading Platform |

**CANDIDATES MUST HAVE FINANCIAL EXPERIENCE**

Java Engineer – FX & Commodities Risk Platform 

Global Investment Bank | Low‑Latency Systems | B2B | Remote | Rate: 2100PLN + | Must have Financial Experience

A global investment bank is expanding its FX & Commodities Risk engineering team and is hiring a Senior Java Engineer in Poland to help build a next‑generation risk platform used by trading desks in London, Singapore, and New York.

This is a rare opportunity to work on a brand‑new system rewrite while learning from a complex, high‑performance legacy platform that has powered global trading for years.

What We’re Looking For

• Strong experience with Core Java and Spring

• Solid understanding of multithreading, concurrency, and low‑latency systems

• Experience with Kafka, Hazelcast, or similar distributed technologies

• Familiarity with modern data stores (ClickHouse is a plus)

• Ability to learn from and navigate complex legacy systems

• Proactive mindset — someone who can drive development forward

• Strong communication and collaboration skills

• FX Risk or broader trading‑systems experience is a strong plus

About the Platform
The FX & Commodities Risk Platform provides real‑time risk calculations and reporting across multiple asset classes. The current system includes:

• Frontend: WPF (C#)

• Backend: Java (Core Java, Spring)

• Quant Library: In‑house, with Haskell used for interaction

A full rewrite is underway, moving the platform to a modern, scalable architecture.

What You’ll Work On
You’ll play a key role in building the new platform, which is moving towards:

• Spring Boot

• Kafka for event streaming

• Hazelcast for distributed caching and computing

• ClickHouse for high‑performance data storage and reporting

Key Engineering Focus Areas

• High‑performance multithreading & concurrency

• Low‑latency backend development

• Distributed systems using Kafka and Hazelcast

• Data‑intensive workloads using ClickHouse

• Interacting with the quant library (light Haskell exposure)

• Learning from the legacy system’s memory‑optimised design (critical for JVM stability)

This is a backend‑heavy role with deep engineering challenges.