Job Description & Details
This is a hands-on Python and full-stack engineering role embedded within an Equity Derivatives Technology team. You'll be building and maintaining the internal Stark platform for Quantitative Investment Strategies, meaning you get to write solid software without needing a PhD in math.
What You'll Actually Be Doing
Your day-to-day will involve bridging the gap between quantitative logic and production-grade software. You will refactor legacy scripts, build interactive web UI components for front-office users, and wire up robust data pipelines using SQL and Pandas. You'll spend plenty of time designing REST APIs, debugging asynchronous Python services, and making sure automated tests actually pass before code hits production.
The Core Tech Stack
You need strong, production-level Python and a deep grasp of object-oriented design, package structuring, and automated testing with pytest. On the UI side, familiarity with modern frameworks like FastAPI, Flask, Streamlit, or even React/HTML/CSS is essential since you'll be building dashboards that traders and quants rely on daily. Strong SQL skills and experience handling financial data—whether through Pandas, PostgreSQL, or market data feeds—will make or break your success here.
Interview Expectations
Expect to be grilled on how you handle concurrency, error recovery, and data integrity in high-throughput Python backends. The interviewer is likely to ask you to design a resilient caching and retry mechanism for an API call that frequently times out, looking for your approach to idempotency and logging. They will also test your database chops, likely throwing a tricky SQL performance tuning or time-series data aggregation problem your way to see how you optimize bottlenecks.
Application Advice
Make sure your resume leans heavily into your Python backend architecture and API integration experience rather than generic web development. Highlight specific projects where you took ownership of a messy codebase, refactored it for scale, and implemented rigorous unit testing using pytest. Drop keywords like REST APIs, Pandas, SQL optimization, and CI/CD pipelines right into your experience bullets to clear the initial automated screening easily.