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: GitHub / GitLab / 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 within the finance industry is a strong plus. Good working knowledge of NumPy 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. #J-18808-Ljbffr