• Build and maintain web app features using React (frontend) and Firebase (backend) • Configure, deploy, and manage Firebase services (e.g., Hosting, Firestore, Auth, Functions as needed) • Implement clean, user-friendly UX/UI and collaborate on product improvements • Create automations to streamline workflows and reduce manual tasks • Use AI tools to speed up development, debugging, testing, and documentation while maintaining code quality • Write maintainable code, handle bug fixes, and continuously improve performance and reliability

• Design, build, and evolve modern, cloud-native fullstack systems across frontend, backend, and infrastructure layers. • Architect and implement scalable RESTful APIs and event-driven backend services that integrate with internal systems and third-party platforms. • Build and maintain mature React and TypeScript frontend applications with a strong focus on usability, performance, accessibility, and maintainability. • Own features end-to-end, including discovery, requirements clarification, technical design, implementation, testing, deployment, and post-release iteration. • Make sound architectural decisions and clearly communicate technical tradeoffs to stakeholders. • Design and optimize relational and/or NoSQL data models to support scalability, reliability, and performance. • Contribute to and improve CI/CD pipelines and release processes to enable frequent, reliable deployments. • Diagnose, resolve, and prevent production issues through strong observability, monitoring, and root cause analysis practices. • Champion engineering best practices, including testing strategy, code quality, documentation, and maintainable design patterns. • Mentor other engineers through code reviews, knowledge sharing, and technical guidance.

• Design, develop, test, deploy, and maintain complex, scalable, and critical backend systems and services capable of handling massive data volumes and high query loads. • Take full ownership of challenging technical projects, driving them from initial concept through to production and ongoing iteration. • Engage with a variety of technical domains, including: Building and evolving RESTful APIs and backend services. • Modifying, extending, and optimizing complex open-source software (e.g., Trino). • Implementing and managing sophisticated identity federation solutions (e.g., Keycloak). • Developing and operating large-scale data ingestion pipelines (e.g., using Airflow). • Enhancing and automating deployment processes (CI/CD) using technologies like Kubernetes. • Collaborate effectively within a team of experienced engineers, contributing to a culture of shared knowledge and mutual respect. • Apply strong problem-solving skills to diagnose, debug, and resolve complex technical issues, ensuring system reliability and performance. • Champion pragmatic and robust solutions, adapting quickly to new technologies and evolving business needs. • Operate with a high degree of independence in a low-process environment, taking initiative and driving results. • Other duties as assigned.

• Embed directly within product engineering teams to drive measurable improvements in developer productivity by integrating AI into daily workflow design, coding, testing, deployment, and operations • Lead tool selection and adoption strategy for AI development platforms (GitHub Copilot, Claude Code, Cursor, etc.), establishing best practices for prompt engineering, context management, and workflow integration • Build and maintain custom AI tooling, MCP servers, and integrations that provide teams with domain-specific context from your codebase, documentation, infrastructure, and business systems • Develop and socialize reusable patterns for code generation, refactoring, test automation, incident analysis, and knowledge retrieval that teams can apply across their daily work • Implement AI code review and validation practices to ensure AI-generated code meets security, quality, and HIPAA compliance standards • Champion a culture of AI-augmented development that enables engineers to tackle bigger challenges, improve code quality, and reduce toil—without sacrificing maintainability or creating technical debt • Create self-service documentation, learning paths, and enablement programs (workshops, office hours, communities of practice) to scale AI adoption across engineering • Measure and communicate impact using both quantitative metrics (velocity, quality, time-to-deployment) and qualitative measures (developer satisfaction, cognitive load reduction, friction logging) • Lead application-level infrastructure-as-code initiatives, empowering product teams to own their cloud resources (containers, SQS, Lambda, DocumentDB, DynamoDB, etc.) • Design reference architectures and terraform/CDK patterns that balance team autonomy with consistency and security • Partner with the enterprise DevOps team to establish clear boundaries—they own foundational infrastructure (VPCs, security groups, IAM foundations), you enable teams to own application-layer resources • Build proof-of-concepts and migration playbooks as GHX transitions from lift-and-shift EC2 environments to truly cloud-native architectures • Manage engineers on cloud-native design patterns, observability, cost optimization, and operational excellence • Provide direction, hands-on execution to grow an engineering team • Work closely with product managers, engineering leaders, and cross-functional partners to translate business needs into practical AI and infrastructure solutions • Establish metrics and feedback loops to continuously improve both AI adoption and cloud-native maturity • Balance innovation with pragmatism—championing new approaches while ensuring solutions scale sustainably.