Key Responsibilities
Technical Design: Lead the design of medium-to-large features across the stack, document decisions, and anticipate edge cases and long-term maintainability
Full Stack Development: Build and maintain TypeScript/Node.js microservices, event handlers, and cronjobs, as well as the React UIs that consume them, powering our core financial flows
AI-Assisted Development: Use AI coding assistants and agentic tools as part of your daily workflow, from scaffolding and refactoring to test generation and code review, while maintaining a high bar for correctness and judgment about when and how much to rely on them
Project Ownership: Break down complex problems into actionable work across multiple services and UIs; drive delivery from design through deployment
Code Quality: Write well-tested, maintainable code; conduct thorough code reviews (including AI-generated code) and hold a high bar for the team
Mentorship: Mentor junior and mid-level engineers, including on effective and responsible use of AI tooling; share knowledge and elevate the team’s technical practices
Cross-Pod Collaboration: Coordinate with engineers in other pods when work crosses service boundaries; contribute code to shared services following established ownership guidelines
On-Call: Participate in on-call rotation for services the pod owns; lead incident response and post-incident reviews
Technical Debt: Proactively identify and address technical debt; balance short-term delivery with long-term health of the systems
Required Qualifications
5+ years of professional software engineering experience, with demonstrated full stack range (backend services and frontend UIs)
Proficiency in TypeScript and Node.js for building production services
Proficiency in React for building production-facing UIs (state management, component design, performance)
Experience designing and building REST or GraphQL APIs
Solid understanding of relational databases (PostgreSQL or similar), modeling, query optimization, and migrations
Familiarity with event-driven architecture and messaging patterns (Kafka, SQS, or similar)
Experience working in a microservices environment
Hands-on, day-to-day experience with AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code, Zed’s Agentic editing, or similar) for coding, debugging, and code review; comfort with prompt/context engineering and sound judgment about validating AI-generated output
Strong grasp of software design principles: separation of concerns, error handling, testing strategies
Experience writing unit, integration, and end-to-end tests
Comfort operating in a cloud environment (AWS preferred), understanding of deployments, logs, and basic infrastructure concepts
Strong communication skills; proactive about sharing progress, blockers, and technical risks
Nice to Have
Experience building or integrating AI/LLM-powered features into products (RAG, agents, tool calling, prompt pipelines)
Familiarity with Kubernetes and container-based deployments
Background in fintech, payments, or financial systems
Experience working with GraphQL (schema design, federation, or tooling)
Knowledge of CI/CD pipelines (Bitbucket Pipelines, GitHub Actions, ArgoCD)
Familiarity with observability tooling (Datadog, Prometheus, Grafana)
Experience mentoring engineers or leading technical design discussions
What We Look For
Beyond the technical skills, we’re looking for engineers who
Take ownership, they drive their work to completion and communicate early when something is at risk
Write code for the next engineer, clean, tested, and documented, whether they or an AI tool wrote the first draft
Improve the team around them, through reviews, mentorship, and knowledge sharing
Collaborate across boundaries, they engage other pods constructively when work crosses service lines
Have good technical judgment, they balance pragmatism with quality, know when to push back on scope, and know when (and when not) to lean on AI tooling
Compensation
Additional Information
