Conduct code reviews and contribute to engineering standards for secure coding, testing, and documentation
Partner with stakeholders to translate business needs into technical designs and production-ready solutions
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
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.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
8+ years of Java experience, including core APIs, web services, concurrency, and design patterns
8+ years of Spring Boot experience building high-performance, maintainable services
5+ years of experience with SQL and NoSQL databases, including data modeling and performance tuning
5+ years of Apache Kafka experience designing and implementing event-driven solutions
2+ years of cloud experience, including deploying and operating services using Amazon Web Services
Proven ability to design, develop, deploy, and support medium to large applications in an agile (Scrum) environment
Proficiency building secure, scalable RESTful APIs and microservices
Experience implementing CI/CD using tools such as Jenkins, GitLab CI, Spinnaker, or AWS CodePipeline
Hands-on experience with automated testing frameworks such as JUnit 5 and Cucumber
Preferred qualifications, capabilities, and skills
Experience with modern DevOps practices and infrastructure-as-code tooling such as Terraform
Experience with performance testing, debugging, and profiling using tools such as JMeter or BlazeMeter
Experience with observability and monitoring tools such as Splunk, CloudWatch, Prometheus, Dynatrace, or Grafana
Experience with containerization and orchestration using Docker and Kubernetes
Demonstrated mentoring and technical coaching experience that supports team growth and continuous improvement
