Senior Engineering Manager

Role Description

We are seeking an Engineering Manager to lead and grow high-performing teams while redefining how modern, AI-native engineering organizations build and ship software. This role is for a leader who has managed teams of 5–15+ engineers and is passionate about building systems where quality is enforced, measured, and continuously improved through automation, observability, and AI-driven workflows. You will be responsible for driving execution, scaling teams, and embedding AI-powered development and testing practices into every stage of the SDLC. Delivering consistently high-quality, production-grade software is a key requirement of this role.

What You'll Do

• Team Leadership & Execution
  • Lead, mentor, and grow a team (or teams) of 5–15+ engineers
  • Drive delivery of software that meets strict, measurable standards for quality, reliability, and maintainability
  • Establish clear expectations where quality is owned by the team and enforced through systems, not heroics
  • Foster a culture of accountability, continuous improvement, and engineering excellence
• Customer Impact & Product Excellence
  • Ensure engineering decisions are grounded in customer outcomes and product impact
  • Partner closely with Product Management to translate customer needs into scalable, high-quality systems
  • Define and track metrics connecting engineering output to customer satisfaction, product adoption, and business outcomes
  • Balance speed, quality, and innovation in service of real-world user value
• AI-Native Quality & Testing Systems
  • Define and implement AI-driven quality strategies across your teams
  • Build and operationalize automated and autonomous testing systems, including AI-generated test cases (unit, integration, end-to-end), self-healing test suites, and agent-assisted validation
  • Leverage LLMs and agent-based systems to continuously expand test coverage, identify edge cases, and reduce manual QA effort while increasing confidence
  • Ensure quality is continuously validated in CI/CD, not deferred to later stages
• Process, Tooling & Observability
  • Design and enforce engineering processes where quality gates are automated and non-bypassable
  • Implement AI-powered tooling across the SDLC: code generation and review assistants, automated code quality and security analysis, and intelligent CI/CD pipelines with adaptive testing
  • Establish comprehensive observability including logging, metrics, tracing, alerting, and SLOs/SLIs aligned with customer expectations
  • Use production data to detect issues early, predict and prevent failures, and drive continuous evidence-based improvement
  • Track and improve key engineering metrics: test coverage, mutation testing scores, defect rates, production incident frequency, and service reliability
• AI-Native Engineering Practices
  • Define and implement AI-first development workflows across your teams
  • Evaluate and integrate modern AI tooling (copilots, LLMs, agent-based systems)
  • Ensure AI adoption increases both velocity and quality
  • Stay current with emerging AI capabilities and translate them into practical engineering improvements
• Technical Strategy & Execution
  • Contribute to and execute the technical roadmap in alignment with business objectives
  • Balance innovation (AI-first approaches) with long-term maintainability
  • Manage technical debt strategically to ensure sustainable velocity and system health
  • Guide architectural decisions that enable scale, reliability, and agility

Qualifications

• 5+ years of software engineering experience
• 3+ years of engineering management experience leading teams of 5–15+ engineers
• Proven track record of delivering high-quality, production-grade systems with measurable outcomes
• Experience defining and enforcing quality standards through automation and systems, not manual processes
• Experience partnering with Product Management to deliver customer-focused solutions

Our Tech Stack

• Languages: TypeScript, JavaScript, Python
• Frontend: Next.js, React
• Backend / Platform: Supabase (PostgreSQL, Auth, Edge Functions, Storage), Node/TypeScript services
• Data: PostgreSQL (Supabase + AWS RDS during migration), Redis
• Auth & Security: Supabase Auth, OAuth2/OIDC, GitHub, Trivy, Snyk
• Infrastructure: AWS, Docker, Kubernetes (for supporting services), modern CI/CD
• AI Tools: Cursor, Devin, GitHub Copilot, and modern agent frameworks where appropriate

AI-Native Mindset

• Hands-on experience with AI-powered developer tools and workflows (e.g., Cursor, Claude, Codex, or similar)
• Strong understanding of how to apply LLMs and agent-based systems to code generation, testing and validation, and developer productivity
• Ability to evaluate emerging AI technologies pragmatically and integrate them into real-world systems

Quality, Observability & Systems Thinking

• Deep understanding of modern testing strategies and quality engineering
• Experience building or scaling automated testing frameworks, CI/CD pipelines with enforced quality gates, and observability systems (metrics, logging, tracing, alerting)
• Experience defining and operating against SLOs/SLIs, reliability and performance targets, and data-driven engineering metrics
• Strong bias toward automation, instrumentation, and continuous validation

Leadership & Communication

• Strong coaching and mentoring skills
• Ability to drive alignment and influence across teams
• Clear communicator across technical and business contexts

Nice-to-Have (Strong Bonus)

• Software Security / Application Security
• Software Supply Chain Security (SCA, SBOMs, CI/CD security)
• Experience in cybersecurity, IoT, or embedded systems domains
• Experience in high-scale, high-reliability, or security-sensitive environments

What Success Looks Like

• Teams deliver consistently high-quality software with measurable improvements in reliability, defect rates, and customer satisfaction
• Automated and AI-driven testing systems provide broad, continuously improving coverage
• Quality issues are detected early—or prevented entirely—through intelligent, data-driven systems
• Engineering velocity increases without tradeoffs in quality, security, or stability
• Teams rely on systems, automation, and observability—not manual effort—to maintain excellence
• Engineering output is clearly tied to customer value and business impact