Engineering Productivity Engineer

We’re looking for an EngProd Engineer to join our Engineering Operations team and own the engineering experience from local setup to production deploy. You’ll build and integrate the tools our engineers rely on every day: local development, code search, testing, code review, CI, AI-assisted workflows, and the metrics that show where time is being lost. You’ll work closely with other engineering teams at Supabase to eliminate friction, optimize cycle times, and make reliably shipping to production effortless.

This role is ideal for someone who thrives in async, fast-paced environments and is excited about building developer tools that engineers love and that empower them to do their best work.

In this role, you’ll:

• Improve local development and the daily workflow
• Own local development environment stability; eliminate “works on my machine,” keep environments reproducible across teams, and make onboarding and setup self-service for every engineer
• Improve the daily editing experience, including IDE and editor tooling, extensions, and code search, indexing, and navigation that stays fast as the codebase grows
• Build paved roads: project scaffolding, templates, and golden-path workflows that make the right thing the easy thing
• Speed up build, test, and CI
• Improve our testing tooling and make sure it works well for engineers, including test orchestration, ephemeral end-to-end environments, and techniques like mutation and chaos testing that catch what ordinary tests miss
• Profile build and test performance, optimize CI pipelines, parallelize and shard tests, cache aggressively, and hunt down the bottlenecks that leave engineers waiting
• Integrate and optimize code review tooling and automated review
• Bring AI into how we build
• Evaluate, integrate, and promote AI coding agents and assistants where they deliver real leverage without sacrificing quality or maintainability
• Build and curate the skills, extensions, and plugins that make those tools effective in our codebase
• Measure the real impact of AI tooling so we invest where it actually pays off
• Drive engineering excellence
• Champion best practices and be a force multiplier for our engineering org
• Track productivity metrics and feedback loops (e.g. DORA) to find where cycle time is lost and improve on it
• Track code-quality signals such as coverage and mutation testing, and build the dashboards that make quality visible
• Establish automation and practices that catch issues early in the development cycle