FX Trading Technology  |  Canary Wharf, London

Contract Length
3 to 6 months, with potential to extend to 12 months

Location
Canary Wharf, London (4 days in the office, 1 day from home)

Salary
£27,000

Start Date
Immediate

Working Hours
9:00am to 5:00pm, with some flexibility on start time (e.g.

8:00am starts)

Reports To
Head of Operations and Business Management, with day-to-day supervision from a Quant on the team

Interview Process
Initial interview (approx.

20 minutes) followed by a Python assessment

About the Business
A leading Electronic Communication Network for global FX trading, forming part of a major exchange group.

The business gives clients, including retail brokers, institutions, banks, hedge funds and proprietary trading firms, access to deep, diversified liquidity through award-winning, ultra-low-latency trading technology capable of handling thousands of clients simultaneously.

The Role
This is a hands-on internship working directly with the Head of Operations and Business Management, and supervised day-to-day by a Quant, on a focused, high-value automation project: replacing a manual invoicing and billing process with a robust, code-based solution.

Today, this process involves pulling data from separate systems, applying exceptions logic, running multi-stage validations, and a significant amount of manual intervention to produce final files.

You will learn the process in full, then build the Python tooling that makes it largely self-running.

Key Responsibilities

• Work closely with the operations team to map the existing invoicing process end-to-end: data sources, transformation logic, exception rules, validation steps and manual outputs.
• Translate that process knowledge into a clear technical specification for the automation solution.
• Build a Python-based pipeline that ingests data from multiple source systems, applies business rules and exception handling, runs the required validation checks, and generates accurate, client-ready invoices.
• Work with a large, data-heavy spreadsheet and encode its logic accurately in code, applying strong attention to detail throughout.
• Reduce manual copy-paste steps through scripted data transformation and output templating.
• Build a robust solution: handle edge cases, surface errors clearly, and keep outputs auditable.
• Document the solution clearly so it can be maintained, extended and handed over.
• Present progress at key milestones to the Head of Operations and other stakeholders.

• Proven, hands-on Python experience, gained through real projects rather than coursework alone
• Comfortable working with Pandas; prior hands-on use is preferred.
• Strong Excel skills: able to open a complex, formula-heavy workbook built by someone else, understand its logic, and translate that into code.
• Experience working with large, detailed datasets, with the attention to detail needed to encode business logic accurately, including exception paths.
• A rigorous approach to validation: you design for edge cases, not around them.
• Clear communication: able to explain what you have built and why to a non-technical audience.
• Follows good coding practice and writes clean, readable, maintainable code.

• Background or coursework in finance, economics, fintech or a related field is a plus, but not essential.