Frontend Engineer · carrier-grade telecom
LotusFlare — DNO Cloud Portal
I own the UIs that let business analysts configure complex data pipelines, reporting and real-time event processing for carrier-grade telecom operators serving millions of subscribers. The work is about making genuinely hard configuration feel manageable — and keeping a large Vue codebase fast and consistent.
Context
The DNO Cloud Portal is how operators serving millions of subscribers configure data pipelines, reporting and real-time event processing. The interfaces are dense and the stakes are high — analysts drive carrier-grade systems through them. My remit is the frontend that makes that power usable.
Performance: 1.2 minutes → 14 seconds
Initial load had crept to roughly 1.2 minutes — unacceptable for a tool people live in all day. I cut it by ~80% to about 14 seconds through dynamic imports, route-level code splitting and Vite build optimizations, shrinking what the browser has to parse and execute before the portal is usable.
Modernizing the codebase
- Led the Vue 3 Composition API + Pinia migration and authored reusable composables adopted across the team.
- Refactored a 2,000+ line SinkConfigEditor into a modular, step-based flow — 45% less complexity, zero regressions.
- Established codebase standards and reorganized imports across 500+ files for long-term maintainability.
- Architected the team's AI engineering stack: cross-IDE agent rules, a CLAUDE.md context system and custom skills.
Features that ship real capability
- A multi-file S3 upload system with real-time progress, smart error handling and DLQ recovery.
- Role-based access control for Reports, with permission-driven UI rendering.
- An Application Management UI with cluster-level actions for data-pipeline orchestration.
The throughline
Two things define the role: making hard configuration feel manageable for analysts, and keeping a large, fast-moving Vue codebase performant and consistent. The 80% load-time cut and the zero-regression refactor are the measurable edges of that work — and the AI engineering stack I set up is how the whole team moves faster on it.