Contributes to open-source projects or technical communities related to GraphQL, platform engineering and AWS services
Scripts and automates using Python and utilizes Terraform and infrastructure-as-code practices for managing complex, multi-environment infrastructure
Architects in GraphQL architecture, schema design, and RESTful API, with experience designing and implementing API standards
Utilizes workflows (Git/Bitbucket) and distributed systems monitoring using tools such as Splunk, DataDog, Dynatrace, or CloudWatch
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
Proven leadership experience in mentoring engineers, leading technical initiatives, and driving architectural decisions across teams
Expert in containerization and orchestration technologies such as Docker and Kubernetes and expert-level proficiency in scripting and automation using Python or similar language (Bash, Groovy)
Deep hands-on experience with Terraform and infrastructure-as-code practices for managing complex, multi-environment infrastructure
Strong expertise in GraphQL architecture, schema design, and RESTful API principles, with experience designing and implementing API standards
Expert-level proficiency with version control workflows (Git/Bitbucket) and distributed systems monitoring using tools such as Splunk, DataDog, Dynatrace, or CloudWatch
Deep understanding of OAuth 2.0, secure authentication/authorization patterns, and security best practices in platform engineering
Extensive experience with AWS cloud architecture and services, including architectural patterns for high availability and disaster recovery
Exceptional documentation skills, including creating comprehensive technical documentation, architecture decision records (ADRs), runbooks, and system diagrams
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
Strong 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 coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities, and skills
Advanced knowledge of AWS services including EKS, ECS Fargate, IAM, VPC design, CloudWatch, X-Ray, ElastiCache-Redis, RDS Aurora Postgres, MSK, and KMS
Proficient in multiple languages (Java, Rust, Go, or similar) with the ability to review code and provide technical guidance
Proven track record designing and implementing comprehensive observability solutions (metrics, logging, tracing, alerting) and automating complex CI/CD workflows using Jenkins, Spinnaker, or similar platforms
Experience with open-source projects or technical communities related to GraphQL, platform engineering, or cloud-native architectures
Experience with AIOps, including deploying monitoring/automation agents to improve observability, reduce alert noise, and increase alert precision
