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The FX Options Pricing and Market Data distribution team is going through significant reengineering and modernisation. We are looking for an experienced Python developer and technologist to take a hands-on role in building out data analytics capabilities and developing frameworks that enable data scientists to leverage market data.
Job Responsibility:
Contribute to the design and development of the electronic FX Options Data Platform
Building out reliable and fast data pipelines for quantitative analysis and trading
Design, build and support our data analytic infrastructure
Collaborate with a global team of developers, quants and traders to translate business requirements into robust technical solutions
Drive the design and development of system architecture, work with users of the system, and enhance the quality of deliverables
Contribute to the continuous improvement of development, deployment and testing processes
Share your knowledge and mentor other developers, fostering a collaborative and innovative team environment
Requirements:
Strong knowledge and experience of Python development
Experience implementing front office systems for Quants and Trading
Proficiency with Python frameworks such as FastAPI, Flask etc for building scalable applications
Experience with Python libraries for financial applications (NumPy, Pandas, asyncio)
Hands-on practical experience delivering system design, application development, testing, and operational stability, particularly in Linux based cloud environments
Openshift/Kubernetes experience with containerized Python applications
Caching technologies e.g. Redis, Memcached or others
Middleware technologies: Kafka, RabbitMQ, Solace, or other message brokers
Experience with Python package management and dependency management (pip, uv, conda)
Awareness of industry trends and leveraging new technologies as appropriate
Proven track record of building new systems, replacing legacy systems and renovating existing systems
Nice to have:
Java and/or C# development experience
Experience with quantitative libraries
Familiarity with CI/CD pipelines for Python applications