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. 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