Shipping a high-scale PWA from an inherited e-commerce project

TL;DR

• A Node.js + Angular e-commerce project at a regional supermarket chain had reached pre-launch with no reliable deploy path.
• I joined as the second engineer on a two-person team, finished the platform, and shipped it as a PWA + mobile apps to a high-volume customer base.
• Along the way we built the Docker image registry and CI/CD pipeline that made continuous delivery possible — for this project and the ones that followed.

Context

A regional supermarket chain in Venezuela had an e-commerce initiative in flight when I joined: there was a Node.js backend, an Angular frontend, and a customer base waiting for it. What was missing was the path from "code in a repo" to "an app customers actually use."

The shape of the work is one any engineer who has joined a pre-launch greenfield will recognise:

• A codebase that ran in development but didn't yet have a repeatable build or deploy path.
• An open question about the customer surface — PWA, native apps, or both.
• Adjacent business needs (customer segmentation, exchange-rate ingestion) waiting on someone with the bandwidth to pick them up.

The brief was simple in one sentence and hard in practice: finish it and ship it.

Constraints

The non-negotiable: ship something customers could use, then build the platform underneath that made the next thing easy.

Decisions

Decision 1 — Ship a PWA first, native apps second

The fastest path to a phone screen wasn't an iOS/Android binary — it was a Progressive Web App that customers could install from a link.

The mobile apps came after, on the foundation the PWA had already proven.

Decision 2 — Build the deploy path before finishing the features

Tempting as it was to chase the feature backlog, the bottleneck wasn't features — it was that nothing could be deployed reliably.

Same principle as similar rescue work: stabilize the runtime before chasing the roadmap. The pipeline outlived the project — the team after me kept using it for the Node.js and Angular services that came next.

Decision 3 — Treat customer segmentation as a small, useful side-quest

Marketing wanted to know which customers to talk to and when. I ran an RFM analysis (Recency, Frequency, Monetary) on the order data the new platform was already collecting.

It wasn't a "data platform." It was a one-engineer analysis that paid for itself the first week marketing used it.

Decision 4 — Automate exchange-rate ingestion instead of typing it in

Pricing in Venezuela depends on the official exchange rate, which someone on the team was copy-pasting daily. Manual rate entry was both error-prone and beneath the pay grade of every person doing it.

This is the seed of the BCV-scraper work I later wrote about publicly. Small automations like this one tend to pay back quickly, mostly because they remove a recurring human failure mode rather than because they save time directly.

Outcome

By the end of the engagement:

• The platform shipped as a PWA + mobile apps used by a high-volume customer base.
• The company had a private Docker registry and a working CI/CD pipeline for Node.js and Angular services — the foundation for everything that came after.
• Marketing had RFM-based customer segments they could act on, drawn from the platform's own data.
• Pricing ran on an automated exchange-rate feed instead of a daily copy-paste.

In hindsight, the sequencing mattered more than the headcount: getting the deploy path in place before chasing the feature backlog is what made the rest of it shippable by a small team.

What I'd do on GCP today

Same brief today, same team size, same constraints. The instincts wouldn't change; the targets would.

• Frontend: ship the PWA the same way, but host it on Firebase Hosting or Cloudflare Pages with a CDN edge in front. Lighthouse perf as a CI gate, not an afterthought.
• Backend: containerize the Node.js services for Cloud Run. No private registry to maintain — Artifact Registry comes with the platform.
• CI/CD: GitHub Actions → Cloud Run, preview environment per PR. The pipeline I hand-built in 2022 is now ten lines of YAML.
• RFM and segmentation: land the orders in BigQuery via a Cloud Run job, run the RFM query as a scheduled view, and surface the segments to marketing through Looker Studio instead of a CSV. Same analysis, no engineer in the loop after week one.
• Exchange-rate ingestion: Cloud Scheduler → Cloud Run job → Pub/Sub → BigQuery + the storefront cache. The scraper still runs; everything around it stops being a cron on a single VM.
• Mobile apps: Capacitor or PWABuilder wrapping the same PWA, signed and shipped through Firebase App Distribution for staged rollouts.

The shape of the work stays the same — deploy path first, customer surface next, the smaller automations after that. GCP just removes a lot of the platform plumbing that used to be the job.

If you're working through something similar and want a second pair of eyes, I'm happy to talk it through — reach out here.