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

• 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

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