Quantitative Developer

We’re looking for a quantitative developer who is passionate about working with financial data and building products that traders and researchers actually use. At Intropic you’ll join a fast-moving fintech startup where engineers own hard, open-ended problems across research, data and product — we don’t just pick up JIRA tickets.

A typical week will mix Python and SQL development, deploying infrastructure changes, building new features for financial-data products, strategic planning with team leads, and attending client meetings to explain work and gather feedback. You should enjoy shipping production-grade code, be comfortable with ambiguity, collaborate closely with analysts and product teams, and take pride in clean, well-tested systems that power real trading and research workflows.

is a full-time role, expected to start in the first half of 2026.

• Collaborate closely with product managers, research analysts and other engineers to define project scope, translate research into product requirements, and deliver concrete technical solutions.
• Maintain, extend and improve Intropic’s suite of financial-data products, from backend data services to client-facing features.
• Design, implement and ship clean, well-tested, production-ready Python code and reusable Python libraries used across the stack.
• Build and maintain data processing pipelines that ingest, transform and validate large and heterogeneous financial datasets.
• Build production REST APIs and data services, and use SQL to analyse large relational datasets.
• Deploy production-quality code to cloud infrastructure (cloud providers, CI/CD pipelines) and own the end-to-end release process.
• Work with analysts to operationalise quantitative research: production-wise models, automate experiments, and ensure reproducible results.
• STEM graduate (or final-year student) with demonstrable coding ability.
• Strong Python skills (other OOP languages such as Java or C++ are welcome and seen as a plus).
• Practical experience with SQL and relational databases
• Comfortable with the command line and modern version-control workflows (example: Git Hub / Git Lab / Bitbucket).
• Strong communicator, able to explain technical work to both technical and non-technical audiences.
• Independent, self-driven learner who takes ownership and can work across disciplines.
• Familiarity with automated testing and general software engineering best practices (code review, CI concepts).
• 0–2 years professional experience in a software engineering, quantitative developer, or data engineering role. Experience with in the finance industry is a strong plus.
• Good working knowledge of Num Py and Pandas.
• Familiarity with backend development and async programming in Python / modern Python frameworks.
• Experience with containerisation and cloud deployments (Docker, cloud platforms such as AWS).
• Practical exposure to financial data via university projects, internships or full-time work.