Lead Frontend Engineer

You own architecture direction for the frontend estate: deciding when a domain should be carved out as an independently deployable unit (a micro-frontend) versus kept inside the core application and defending that call against both premature fragmentation and unchecked monolith growth.

You set technical direction on composition strategy for a multi-team frontend (build-time integration versus runtime composition such as module federation), weighing deployment independence against operational, versioning and performance cost, and you can make the case against a micro-frontend split when the cost outweighs the benefit.

You weigh SPA, server-side rendering, static generation and edge rendering against each other for a given page or flow, choosing based on real constraints (SEO, time-to-interactive, data freshness, device and network conditions) rather than defaulting to whatever the framework does out of the box.

You can trace jank through the actual rendering pipeline (parse, style, layout, paint, composite), know which changes trigger a full reflow versus a cheap compositor update, and keep the main thread free for user input using scheduling techniques or web workers.

You move Core Web Vitals (LCP, INP, CLS) in production, not just report on them, confirming every gain against real user monitoring rather than lab scores alone.

You know when a piece of state belongs in a local component, a shared client store, or a server-state cache, and you treat the URL as a first-class store for shareable, bookmarkable state so deep links, refreshes and browser back/forward all land on the correct view.

You treat every advanced browser platform capability (web workers, service workers, IndexedDB or the Origin Private File System, WebAssembly) as a deliberate architectural decision backed by a documented cost-benefit case, not a default reach because the browser supports it.

Comprehensive design system experience, with a history of building and governing design systems used across multiple squads.

Demonstrated success driving AI-assisted engineering as standard practice — identifying opportunities to improve developer productivity, quality, and delivery efficiency, and leading that adoption.