Work

NinjaCat · Forward Deployed Engineer

July 2026 – Present

Strategy Labs · Marketing Data Specialist

March 2021 – July 2026

I ran data at Strategy Labs, a boutique digital marketing agency, for 5 years. Started in the marketing-data plumbing — Funnel.io pipelines, Supabase warehouses, reporting infrastructure — then spent my final years transitioning to AI tools and systems that helped turn the agency’s data and context into something agents could reliably operate on.

  • Built agentic workflows that changed how the team worked — assembled client performance summaries, source-backed, reviewed by a human, sent. Same info, fraction of the time.
  • Shipped SCOUT — a data analytics agent for marketing agencies that bridged the gap between raw data warehouse fields and actual business meaning via a semantic layer and ontology system I designed. Used across 20+ clients and 100+ data sources for the internal team, spanning structured and unstructured data.
  • Built the Contribution Margin Forecaster — a profitability calculator that connected business-level profit metrics to marketing inputs in real time. Revenue, ad spend, CPC, CTR, AOV, CPM — the model forecasted how each input backed into actual profit, not just ROAS. Gave clients a live profit-tracking view and gave the marketing team KPI goals tied to margin, not just channel efficiency.
  • Helped build Benchmarketing — the cross-brand performance comparison engine. Strategy Labs worked with multiple brands in the same vertical, so the pitch was simple: anonymized benchmarking across similar businesses showing where you sit on efficiency, growth, and margin relative to peers. I worked on the data architecture: normalizing metrics across different ad platforms and commerce stacks so a blended CAC from one brand meant the same thing as another’s, then building the view layer that made the comparison readable and actionable.
  • Drove adoption across CSM, Creative, Data, Media, and HR — agents embedded in real workflows, not demos. Also the human part: skills people reached for, educating the team, and communicating the why behind going AI-native.

What the work covered

  • Data platform architecture — schema design, ingestion pipelines (Funnel.io), Supabase warehouses, and the query and reporting layers on top.
  • Applied AI product development — shipping AI-native products with LLMs (primarily Claude), agent frameworks, and workflow skills. Prototypes, but also production systems with real users.
  • Internal AI adoption — training teams, embedding agents into workflows, building internal tooling that made AI-generated data usable by people who’d never written a prompt.
  • Business-to-data translation — working with Ops and leadership to surface the right questions, then building the infrastructure to answer them.
  • Automation and integration — pipelines connecting data across ad platforms, CRMs, e-commerce, and internal tools.
  • Marketing operations and growth — paid media, CRO, and attribution work. The data infrastructure work grew out of those marketing problems.

Education

Business Administration with Marketing Concentration, Western Washington University, 2016 — 2020