Identify opportunities to improve the team's development, testing, support and delivery processes

Design and implement practical automation solutions that reduce manual effort, improve quality and increase confidence in delivery

Use AI-assisted development and automation tools to improve requirements refinement, testing, documentation, diagnostics and engineering workflows

Evaluate emerging technologies and assess where they can be applied pragmatically within the team

Build practical, working solutions that can be adopted by the team, rather than producing concepts or recommendations alone

Analyse production incidents, identify root causes and turn recurring issues into opportunities for improvement

Promote engineering practices that enable the team to deliver more consistently, efficiently and with stronger controls

Experience designing, building and delivering production-grade applications or automation solutions

Strong analytical and problem-solving skills, with the ability to investigate complex systems and data flows

Experience building automated testing frameworks or improving test coverage for existing systems

Practical experience with at least one major programming or scripting language, such as Python, Java or a functional programming language

Basic experience with databases, SQL, data analysis or reporting tools

Comfortable working in a Linux environment and using command-line tools

Familiarity with Jira, Git, code review processes or team documentation tools

Willingness to learn Q/KDB+ and apply it to the team's use cases

AI-assisted development, workflow automation, data analysis automation or prompt-based productivity tools

Application architecture and system integration patterns

APIs, interfaces and distributed systems

Databases and data flows

Batch and real-time processing concepts

Production support processes

Financial markets, risk data, trading platforms or regulatory reporting environments