You’ll be building a platform used by analysts who oversee the integrity of U.S. financial markets. The work sits at the intersection of complex data systems and user-facing design—turning sophisticated data pipelines into tools that non-technical users can operate with confidence. Your systems will be powered by the largest cloud-hosted dataset in the financial regulatory space, processing billions of market events daily. This is a greenfield opportunity to shape the architecture from the ground up, working with a team that values strong engineering practices and healthy work-life balance.
Job Responsibilities
- Partner with product and design teams to build applications that enable users to work more efficiently and effectively
- Contribute to the design and implementation of scalable solutions using industry standards and best practices
- Develop and maintain robust back-end systems that integrate with existing user platforms
- Support the team in shepherding releases through the full engineering lifecycle, from development to production
Qualifications
- Bachelor’s degree in a technical field such as Computer Science, Computer Engineering, or a related discipline
- 5+ years of experience building production-quality, web-based, multi-tier applications
- Strong experience building RESTful web services using Java (Spring), Python (Django, Flask, FastAPI), or Scala (Play)
- Experience integrating back-end services with modern JavaScript front-end frameworks such as Angular
- Experience building data platforms or data analytics web applications
- Strong SQL experience in big data environments, including writing and analyzing complex queries
- Familiarity with Amazon AWS services, including use of the AWS console and command-line tools
- Experience working in Agile/Scrum environments using CI/CD development practices
- Experience writing automated unit, component, and integration tests
- Ability to adapt to evolving business priorities and changing requirements
Nice to Have
- Experience building data pipelines integrated with machine learning models
- Hands-on experience with big data technologies such as Spark, Kafka, Flink, or similar tools
- Familiarity with Apache Airflow or comparable orchestration frameworks
- Experience using AI throughout the development lifecycle, including AI-assisted coding, spec-driven development, or workflow automation tools
