Full-stack SW Engineer – Back end focused

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.

• 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

• 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

• 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