Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
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
Formal training or certification on software engineering concepts and 2+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Experience developing, debugging, and maintaining code using Java and Spring Boot in a corporate or production environment
Working knowledge of React or a comparable JavaScript framework for building responsive, user-facing web applications
Hands-on experience with Kubernetes for container orchestration, deployment management, and application scaling
Hands-on experience using enterprise-authorized AI-assisted software development tools (e.g., for coding, test creation, troubleshooting, or documentation), with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
Overall knowledge of the Software Development Life Cycle
Solid understanding of agile methodologies, including CI/CD, application resiliency, and security best practices
Preferred qualifications, capabilities, and skills
Exposure to payments or financial services domain concepts such as transaction processing, clearing, settlement, or liquidity management
Familiarity with cloud infrastructure technologies including compute, storage, container services, and serverless functions (e.g., AWS, Azure, or GCP)
Experience with observability and monitoring tooling such as Datadog, Grafana, or Splunk for production system health and incident response
Knowledge of REST API design principles and microservices architecture patterns
Experience contributing to agile ceremonies, sprint planning, and cross-functional engineering collaboration
