Requirements

Formal training or certification on software engineering concepts and applied experience
Recent hands-on professional experience as a back-end software engineer
Experience coding in a recent version of the Java programming language
Experience designing and implementing effective tests (unit, component, integration, end-to-end, performance)
Excellent written and verbal communication skills in English
Experience with cloud technologies and distributed systems, RESTful APIs, and web technologies
Knowledge of messaging frameworks
Experience operating, supporting, and securing mission critical software applications
Understanding of different kinds of data stores, including relational data stores
Ability to coach team members on coding practices, design principles, and implementation patterns
Ability to manage stakeholders and effectively prioritize work across multiple work streams
Experience leading use of enterprise-authorized AI-assisted software development tools with ability to set validation standards for AI outputs
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, security, and resiliency considerations, and experience coaching senior engineers on compliant usage

Nice to Haves

Deliver end-to-end cloud-native microservices architecture applications leveraging latest technologies and industry best practices
Use domain modelling techniques to build best-in-class business products
Structure software for ease of understanding, testing, and evolution
Build scalable solutions that avoid single points of failure
Develop secure code to protect customers and the business from malicious actors
Investigate and fix issues promptly to prevent recurrence
Ensure zero downtime releases for end-users
Optimize data writing and reading methods according to business needs
Monitor and improve performance by identifying and solving problems
Ensure systems are reliable and easy to operate
Continuously update technologies and patterns to stay current
Support products throughout their entire lifecycle including production and incident management
Drive adoption and governance of AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes
Apply knowledge of software development life cycle tools and AI-assisted automation to improve automation value at scale