About the FirmThe hiring firm is a long-established international financial institution known for blending deep industry heritage with a progressive, entrepreneurial mindset. Unlike many large organisations, the London team operates with the pace and cohesion of a much smaller unit – where ideas move quickly, technology has genuine influence, and individuals are encouraged to take ownership from the outset.The firm has built its reputation on thoughtful innovation, high-quality execution, and a belief that talented people thrive when given both trust and responsibility. Technology and data sit at the heart of how the investment business operates, and the firm is looking for a graduate who wants to contribute to that philosophy from day one.Role OverviewThis role sits within a specialist investment technology group that partners closely with portfolio managers, quantitative researchers, and operational teams. It’s an opportunity to help shape and refine the systems that power the firm’s investment platform – not simply maintaining existing tools, but actively contributing to how things are built, scaled, improved, and automated.The successful candidate will work across data engineering, analytics, infrastructure, and workflow design. The environment is collaborative, intellectually curious, and places a real premium on craftsmanship – whether that’s writing clean code, designing thoughtful data processes, or finding elegant solutions to complex problems.Key ResponsibilitiesDevelop and evolve internal technology used across trading, research, and risk – with real visibility and impact.Build, refine, and automate data pipelines that bring together market data, financial statements, and alternative datasets.Support quantitative research through scripting, exploratory analysis, and data preparation.Create dashboards, visualisation tools, and interactive analytics that help investment teams interpret information more effectively.Drive automation initiatives that streamline operational, compliance, and reporting workflows.Contribute to the continued enhancement of trading and execution tools with a focus on efficiency, reliability, and usability.Uphold high standards of testing, documentation, and version control – treating internal systems with the care of production-grade software.Investigate performance issues, data anomalies, and system behaviour with a problem-solver’s mindset.Work collaboratively across investment, risk, and operational teams to understand challenges and craft tailored solutions.Skills & QualificationsStrong academic background in Computer Science, Data Science, Engineering, Mathematics, Physics, or a related technical discipline.Confidence in at least one modern programming language – ideally Python, with SQL, Java, or C++ as a plus.Understanding of data structures, software engineering fundamentals, and how systems interact.Curiosity about markets, technology, and the way data shapes decision-making.Clear analytical thinking, attention to detail, and an ability to translate complex ideas into practical solutions.Experience with Git, databases, or cloud environments is helpful, but not required.Comfortable in a fast-moving environment where ideas are welcomed, initiative is expected, and collaboration is key.A proactive, self-directed approach – the firm values those who take ownership rather than wait for instruction.What the Firm OffersImmediate exposure to real investment and trading systems – genuine responsibility, not simulation.A chance to design and deliver tools that directly influence how decisions are made.A flat, collaborative culture where graduate voices are heard and good ideas move quickly.Mentorship from highly experienced individuals across technology, quant research, and investment.Broad exposure to the full ecosystem of investment operations: data engineering, trading, risk, analytics, and automation.A clear pathway to grow into senior engineering, quantitative, or hybrid technical roles.An environment where curiosity, craftsmanship, and initiative are genuinely valued. #J-18808-Ljbffr