Upwork home
Sign up
Secondary
Jason L. Status: Offline
Marietta, United States
97% Job Success
Top Rated

Marketing Technologist & Data Architect

I turn scattered marketing data into a single, revenue-driving source of truth. For the past decade I’ve built and scaled attribution engines, server-side tracking stacks, and automation workflows for brands that spend six to eight figures on ads. My specialty is stitching every click, session, and purchase—web or app, online or offline—into one clean ledger that ad platforms, BI tools, and executives can finally trust. What I deliver • Accurate, cross-device attribution – deterministic IDs first, probabilistic backups, zero credit leakage • Server-verified conversions – Cloud Functions/Lambda endpoints, de-duplication, offline uploads to Google Ads, Meta, TikTok, SKAN • Actionable journey mapping – Users → Sessions → Events modeled in BigQuery or Supabase, surfaced in Looker Studio and Power BI • AI-driven optimization – predictive LTV scoring, anomaly detection, and auto-bidding scripts that adjust spend before budget burns • Workflow automation – Zapier, n8n, and custom Python orchestration that slash data-ops time from hours to minutes Tech I speak fluently Google Tag Manager | Google Ads & GA4 APIs | Appsflyer | Segment | BigQuery | Supabase | Python | JavaScript | Cloud Functions | Looker Studio | Zapier | Airflow | AWS & GCP When an enterprise brings me in for a full-scale attribution and automation overhaul, three categories of outcomes follow—structural, financial, and cultural. 1. Structural gains • One canonical data spine—every click, device, login, and transaction tied to a single customer key and stored in a warehouse the entire org can query. • A hardened server-side event pipeline that de-duplicates, validates, and routes conversions to Google, Meta, TikTok, affiliate networks, and downstream BI tools within seconds. • Production-ready documentation, runbooks, and monitoring dashboards so Engineering, Marketing, and Compliance share the same map and alerting. • Privacy and regulatory guardrails baked in (GDPR, CPRA, state gaming rules, Apple SKAN), reducing legal exposure before it surfaces. 2. Financial impact • 15–30 % cut in wasted ad spend as smart-bidding algorithms finally learn from true revenue, not last-click noise. • Faster payback periods on campaigns because late or offline conversions are captured and credited back to the original spend. • A clear line of sight from marketing cost to customer lifetime value, enabling budget reallocation at quarterly planning without guesswork. 3. Cultural shift • Marketing stops debating whose spreadsheet is “right” and starts iterating on creative, offers, and channels supported by a single source of truth. • Data and Engineering teams reclaim hours previously spent reconciling reports; that time moves to high-leverage analysis and automation. • Leadership gains real-time KPIs—cohort LTV, channel ROAS, churn risk—refreshed automatically, sparking evidence-based decisions at board level. In practice, enterprises see campaign insights that used to take weeks surface overnight, promotions tuned mid-flight, and cross-functional teams rallying around shared metrics instead of siloed reports. The stack I leave behind is modular and documented, so your internal teams can extend it—adding new regions, business units, or predictive models—without another ground-up rebuild.
Work history
Jason L. has more jobs. Create an account to review them
Skills

Skills

Browse similar freelancers