Full Stack Software Engineer
A leading quantitative trading firm is seeking a Software Engineer to join a team that sits at the intersection of technology, data, finance, and trading. This group is responsible for building the tools, systems, and data infrastructure that support critical business operations and help turn complex datasets into actionable insights.
Working closely with stakeholders across multiple global offices, the team develops automation, analytics, and reporting solutions that drive efficiency and decision-making throughout the organization. This is an opportunity for an engineer who enjoys building scalable backend systems, working with large datasets, and partnering directly with business users to solve real-world problems.
What You'll Do
- Design, develop, and maintain business-critical applications, automation tools, and internal systems.
- Build and optimize data pipelines that support large-scale data storage, retrieval, and analytics.
- Create dashboards and visualization tools used by stakeholders across a global organization.
- Partner closely with finance, operations, and trading teams to gather requirements and deliver technical solutions.
- Improve system performance, streamline workflows, and drive operational efficiency through automation.
- Contribute to development best practices, system architecture, and ongoing platform enhancements.
Requirements
- 2-5 years of software engineering experience.
- Strong Python and SQL development skills.
- Experience building backend systems and working with large datasets.
- Hands-on experience with modern engineering tools and AI-assisted development workflows.
- Familiarity with CI/CD pipelines and software development best practices.
- Experience working with containerized environments such as Docker or Kubernetes.
- Cloud experience required; AWS exposure is a plus.
- Strong communication skills and the ability to collaborate with technical and non-technical stakeholders.
Preferred Experience
- Experience with React or frontend development.
- Exposure to financial systems, trading environments, or business intelligence platforms.
- AI/ML engineering experience.
- Background working in fast-paced, data-driven organizations.
FAQs
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