Applies technical troubleshooting to breakdown solutions and solve technical problems of basic complexity
Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development
Learns and applies system processes, methodologies, and skills for the development of secure, stable code and systems
Required qualifications, capabilities, and skills
Bachelor’s Degree in Computer Science, Cybersecurity, Data Science, or related disciplines
2+ years of formal experience or certification on software engineering concepts
Hands-on practical experience in system design, application development, testing, and operational stability
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
Experience across the whole Software Development Life Cycle
Exposure to agile methodologies such as CI/CD, Application Resiliency, and Security
Preferred qualifications, capabilities, and skills
Hands-on development experience with Java and Spring Boot/Spring Core, including building REST APIs and integrating with downstream systems
Working knowledge of React (or strong willingness to contribute to UI components as needed) and test automation using JUnit
Familiarity with event-driven concepts and tooling (e.g., MQ and/or Kafka) and distributed system fundamentals (retries, timeouts, idempotency)
Exposure to caching technologies (e.g., Redis and/or GemFire) and performance-minded development practices
Working experience with Agile delivery and tooling: Jira for execution tracking and Confluence for documentation
Basic cloud/platform exposure and willingness to learn: AWS, Kubernetes, Terraform (hands-on experience preferred but not required depending on team needs)
Strong engineering fundamentals: clean code, code reviews, debugging, and clear written communication
Experience in payments or regulated financial services, especially high-availability and low-latency systems
Experience operating production services (incident response, post-incident reviews, observability, runbooks)
Familiarity with secure SDLC practices, including vulnerability remediation and dependency hygiene
Exposure to AI/LLM-enabled development workflows (used responsibly) for productivity improvements (e.g., code assistance, test generation, documentation drafts)
