Shopify & E-commerce Engineer
I engineer Shopify storefronts across the full buying path.
From the offer and product decision to cart, checkout, measurement, and safe production delivery. On stores that stay live the whole time.
Featured project
Lumway: Engineering a conversion-focused Shopify storefront
A US wellness DTC brand. One system, six engineering stories that together make the purchase journey fast, trustworthy, and measurable.
- 6Deep dives
- ~130Sections
- ~180Snippets
- 80Templates
- 6Deep dives
- ~130Sections
- ~180Snippets
- 80Templates
Reusable Liquid files across the Lumway theme and its templates, not a single page.
View main case study
Commerce State & Cart
Solved three silent cart failures caused by page-wide DOM queries and third-party apps.
Subscription Buy Box
Built a custom subscription experience on native plans without hardcoded IDs.
Performance & RUM
Found and fixed a ~0.9 CLS spike that lab tools never reproduced.
- Loop → Recharge MigrationMigrated 66 subscriptions with Shop Pay payment methods.
- Production-safe DeliveryWorkflow that protects live content and prevents overwrites.
- Analytics ArchitectureFull-funnel instrumentation across the checkout boundary, with customer names kept out of the payloads.
Selected Shopify work
Different constraints. The same engineering discipline.
From a medically complex catalog to a disciplined premium-theme adaptation, each project required a different balance of custom experience, native Shopify behavior, and merchant control.
Absolute Support
Extending Dawn for a Complex Compression Catalog
A custom product-selection and educational layer for a medically complex catalog, while Shopify's native variant, cart, search, and filtering systems remained the source of truth.
- Compression modeled separately from purchasable variants
- Custom presentation feeding Dawn's native state
- Merchant-editable OS 2.0 content layer
Boxwood Coffee
Knowing What Not to Rebuild
A disciplined Prestige adaptation for a specialty roaster spanning retail, subscriptions, two cafés, catering, and wholesale, with custom code reserved for coffee-specific data and navigation.
- Configure, extend, or build
- Coffee product data modeled with metafields
- Prestige commerce behavior preserved
Commerce engineering
Engineering around how a store actually sells
I work beyond the theme layer: mapping the purchase path, identifying where trust or commerce state breaks, adding the measurement needed to understand it, and changing the smallest part of the system that can improve it.
Find where the purchase path breaks
I trace the path from offer to order and find where the visible experience and commerce state stop matching.
Purchase path
Offer
Pricing, packs, savings
Product decision
Variants, subscription, education
Cart
State, quantity, drawer
Checkout
Events, delivery, payment
Order
Confirmation and follow-up
Common breakpoints
Stale pricing · Mismatched variants · Silent cart failures · Mobile instability · Missing events · Checkout gaps
Choose the right level of customization
I decide whether a requirement should be configured, extended, integrated, or built.
Configure
Use the platform where it already solves the requirement.
Extend
Adapt the native system with theme presentation and structured data.
Integrate
Connect apps and external systems without duplicating ownership.
Build
Add custom behavior only where the requirement genuinely needs it.
The goal is not maximum customization. It is the smallest reliable solution.
Create the measurement loop
When existing analytics are insufficient, I add the instrumentation needed to observe, diagnose, change, and verify.
Observe
Real-user behavior and events
Diagnose
Find the root cause
Change
Implement the smallest useful fix
Verify
Measure the engineering outcome
Loop continues as new evidence appears.
RUM · Checkout Web Pixels · Browser verification · Operational checks
Commercial impact is claimed only when the relevant analytics are available.
Improve the store without making it harder to run
A commercially useful improvement must also survive daily merchandising, app integrations, theme updates, and future releases.
Merchant ownership
Routine content remains editable.
Structured data
Products and collections carry their own information.
Safe delivery
Changes remain isolated and revertable.
Compatibility
Custom work respects the platform, theme, and installed apps.
A better storefront should not become a harder store to operate.
Commerce levers
Where those engineering decisions show up in the storefront.
Commerce decisions I can translate into the storefront
Offer structure
Packs, pricing, savings, subscription cadence, and one-time purchase.
Product decision
Variants, attributes, sizing, education, and comparison.
Discovery
Taxonomy, collections, filtering, merchandising, and product finders.
Purchase continuity
Buy-box state, cart behavior, checkout boundaries, and mobile flow.
Measurement
Events, real-user performance, controlled testing, and operational verification.
I can implement, instrument, and iterate on these decisions with the business team.
AI-assisted execution
I use AI to accelerate repository exploration, implementation planning, test drafting, documentation, and repetitive development work. It supports the workflow; it does not decide the commerce model, own production risk, or replace verification.
Where it helps
- Repository exploration
- Implementation planning
- Test drafting
- Refactoring assistance
- Documentation
- Data transformation
- Prototyping
- API and workflow experimentation
Verification boundary
Generated output is reviewed against the codebase, platform constraints, and production behavior before it is treated as implementation.