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The work speaks for itself.

Dated velocity metrics and the work behind them. Every number is real and verifiable on request.

Several projects below were completed during a multi-year engagement with a single client under NDA. The technical receipts are real. The client name stays private. If you need specifics, we can discuss under your own NDA.

FILES

27

YEARS

7

DOMAINS

5

UNDER NDA

14

$ wc -l case-studies/*
CLOUD07TRACKING06SHOPIFY06AI / AGENTS05BRAND04
// five domains. 28 receipts.

Three files worth the open.

Pick any of the three to see the full brief, the work, and the receipts. Or scroll for the archive below.

FILE 01/CLOUDQ3 2024
FILED UNDER NDA

The Silent Failure

A legacy cloud integration layer had stopped syncing. I traced it, patched it, brought it back.

A client's partner integration layer had gone dark with no alerting. I diagnosed the underlying cause in an AWS network configuration, rotated the affected credentials across the cloud function estate, and brought downstream data flows back online the same afternoon with zero data loss.

Hours

Diagnosis to full recovery

Full

Cloud function estate restored

3,000+

Records backfilled same day

0

Data loss events

OPEN FILE
FILE 02/TRACKINGQ2 2024
FILED UNDER NDA

The Tracking Gap

Browser-only tracking was missing half the story.

An ecommerce brand was making ad spend decisions against incomplete data. iOS 14.5 had silently broken browser-only pixel tracking. I built the entire server-side Meta CAPI infrastructure from scratch in 48 hours: GTM web and server containers, Stape cloud hosting, custom loader domain for first-party cookies, 11+ event tags with full event_id deduplication and external_id hashing.

+35%

Match quality improvement

9.1

Post-implementation MQ score

48h

Scratch to production

0

Revenue interruption

OPEN FILE
FILE 03/TRACKINGQ1 2026
FILED UNDER NDA

The Analytics Engine

Commerce, web, ads, and email data unified under one roof.

A client was pulling marketing and commerce data from multiple disconnected platforms, each with its own login, its own metrics definitions, and its own version of the truth. I built an enterprise analytics platform with a multi-service data pipeline: separate ingestion, warehouse (ETL), API service, and web UI layers, all managed through a Turbo monorepo.

Unified

Data pipeline

Several

Production dashboards

Multiple

Data sources

1

Quarter to ship

OPEN FILE

The Rest of the Cabinet.

CLOUD/05TRACKING/05BRAND/09SHOPIFY/02AI / AGENTS/06

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