
Posted 17 days ago
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Full Stack Engineer role focused on Angular and Python to support enterprise risk technology for the CIO team. The position combines web development (Angular, Typescript, NextJS, FastAPI) with Python quantitative tooling and asynchronous programming for performance and scalability. Responsibilities include collaborating with quant teams, building reusable code packages, automating tasks, and working with large/time-series datasets and upstream data providers. Preferred experience includes data engineering libraries (Pandas, Polars, NumPy), AWS (EC2, S3, Redshift), and prior quantitative/finance exposure.
- Compensation
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Currency: Not specified
Full Job Description
- Design, develop, and maintain web applications using Angular and FastAPI.
- Implement asynchronous programming workflows to enhance application performance and scalability.
- Develop Python applications and tools for quantitative analysis and modeling.
- Build functional Angular components using Typescript and NextJS.
- Collaborate with quant teams to understand and implement their requirements.
- Identify and create reusable code packages.
- Document development phases and monitor systems.
- Automate tasks through appropriate tools and scripting.
- Collaborate with internal teams and upstream internal data providers.
- Stay up to date with industry trends, emerging technologies, and best practices.
- 3+ years of professional software engineering or development experience.
- 3+ years of professional experience with Angular, or other typescript frameworks will also be considered.
- Bachelor's degree in Computer Science or related field.
- Strong understanding of synchronous and asynchronous programming and their applications.
- Strong understanding of data structures, data modeling, and software architecture.
- Excellent analytical and problem-solving skills.
- Proficient in both written and verbal English communication.
- Ability to work independently and as part of a global team.
- Knowledge in Math and statistics.
- Familiarity with data engineering libraries such as Polars, Pandas, Numpy, etc.
- Experience working with large datasets and time series data.
- Experience working with AWS (especially EC2, S3 and Redshift).
- Knowledge in finance.
- Prior experience in quantitative development.





