Tableau & marketing analytics
Tableau dashboards built with SQL that power weekly GTM reporting. Ties media metrics to CRM outcomes so teams see what's driving pipeline and what to revise next.
Marketing analytics + AI workflows
I build GTM analytics systems that turn messy data into action.
I connect paid media, CRM, and funnel data into governed GTM reporting via Tableau. I then layer in agentic analytics so GTM teams get budget and pipeline answers without filing another ad-hoc request.
Marketing Analytics EngineerChicago, IL
6+ years on marketing data infrastructure, Tableau/Power BI, and GTM reporting
Paid search, paid social, SEO, Salesforce, dbt, Redshift, and BigQuery in production
Builds Claude and Cursor workflows so non-technical teams can self-serve governed metrics
What I build
Reporting and workflows tied to spend, pipeline, and tests.
AI-assisted GTM workflows
Repeatable pipelines for SEO, competitor monitoring, outbound, and paid media with human review where it matters.
Measurement for decisions
Attribution, incrementality, media mix modeling, and test design framed as what to scale, pause, or fix next.
Experience
Roles, scope, and outcomes.
Spend figures are anonymized ($XXk, $XXX million) so you can see scale.
Marketing Analytics Manager
ADP · March 2022 - Present
- Run Tableau performance marketing dashboards on Salesforce and paid media data that cut cost per lead 20%.
- Inform allocation across $XXX million in annual digital spend with media mix modeling (MMM), multi-touch attribution (MTA), and channel-level performance analysis.
- Run A/B testing infrastructure for paid search bid strategies that improved conversion rates 30%.
Marketing Analytics Manager
Dentsu / iProspect · July 2021 - March 2022
- Built incrementality and attribution models for telecom paid search at $XXX million in annualized media.
- Ran 50+ tests on bidding, creative, landing pages, and audiences.
- Led weekly reporting for senior marketing stakeholders with clear channel narratives.
Analytics Consultant
LR Marketing Analytics · September 2020 - July 2021
- Delivered 20+ Power BI dashboards for healthcare and software clients across prospect through nurture.
- Sized and analyzed $XXk to $XXXk per month in digital media.
- Reworked Marketo lead scoring and automation; MQL-to-SQL conversion rose 25% in two months.
Performance Marketing Specialist
OpGo Marketing · January 2020 - July 2021
- Built Power BI and Google Data Studio reporting for 20+ client ad accounts.
- Moved recurring exports into Power BI Service and cut 5+ hours per week of manual spreadsheet work.
- Managed and reported on $XXk per month in media across 25 accounts.
Toolbox
Tools I use in production.
Selected work
Case studies and repos with context.
Marketing analytics
Performance Marketing Tableau Dashboard
Wireframe-first Tableau case study: weekly paid media and CRM reporting for budget and funnel decisions, with anonymized spend.
- Problem
- Web analytics, paid media platforms, and CRM outcomes each live in their own tool, so teams end up juggling five disconnected sources of truth. There is no single place to see how a click turned into a lead and whether that lead became pipeline, which means every question to leadership starts with exporting and stitching spreadsheets by hand.
- Approach
- Model Paid Search, Paid Social, GA4, and Salesforce data in dbt on Redshift so every channel reports against one funnel definition in SQL. Lay out Tableau so a weekly scan compares channels side by side and surfaces the next test worth running: audience cuts, creative angles, landing pages, or channel mix.
- Outcomes
- A connected view turns the weekly scan into a short list of specific tests worth running next: which creative is fatiguing, which audiences convert, where to shift mix. Each test closes out and feeds the next week's read.
- Step 1Paid media + SFDC sources
- Step 2dbt mart
- Step 3MetricFlow Semantic Layer
- Step 4Claude via MCP
Output Plain-English CPL, ROAS, and pipeline reads that match reporting
Agentic analytics
Claude on dbt Mart Tables
Media + CRM data staged in dbt mart tables. MetricFlow Semantic Layer defines metrics like CPL and ROAS once. Claude answers executive questions in plain English through MCP, in minutes instead of hours or days.
- Problem
- Leadership asks for CPL, ROAS, or pipeline in the meeting, then waits on exports while paid media, Salesforce, and web data get reconciled. Each request pulls analytics off their roadmap.
- Approach
- Model paid media and Salesforce in dbt (staging, intermediate, marts). Define CPL, ROAS, and spend once in MetricFlow's semantic layer. Connect Claude to those definitions through the dbt MCP server so ad-hoc questions return the same numbers as reporting.
- Outcomes
- Executives get answers in minutes on questions like "CPL by platform last quarter" or "campaigns with rising spend but flat pipeline." Ad-hoc volume drops, and analytics gets time back for roadmap work instead of spreadsheet rebuilds.
- Step 1Topic intake
- Step 2SERP research
- Step 3BriefHuman review
- Step 4Draft
- Step 5QAHuman review
- Step 6Publish
Output Deploy-ready article package with brief, draft, and QA notes
Agentic analytics
SEO Agent Pipeline
Ten-stage SEO pipeline: research, draft, QA, and publish steps with human-in-the-loop approval gates before anything ships.
- Problem
- SEO work stalls when research, briefs, drafts, and deployment live in different tools and nobody owns the handoffs.
- Approach
- Split the work into stages. Agents handle research and drafts and generate artifacts after each stage. Humans approve outputs before moving to the next stage and deploy. By staging each step, we never overwhelm Claude's context window.
- Outcomes
- Significantly increases velocity to publish high-ranking content while keeping brand voice and quality intact through human gates at brief and final review.
- Step 1Name competitor
- Step 2Rival Ad Library pull
- Step 3AI + human review
- Step 4Ad CatalogHuman review
Output Brief-ready PDF, ad catalog with every rival asset, and tests to run on your ads
Agentic analytics
Mako Metrics: Meta Ads Competitor Spying
Competitor Meta ad research: AI reads a rival's public Ad Library, a human reviews the analysis, and you get a brief-ready PDF plus an ad catalog with their creatives, copy, and CTAs.
- Problem
- Teams need to study a competitor's live Facebook and Instagram ads before a creative brief, but scrolling Meta's Ad Library for one brand is slow and easy to misread.
- Approach
- The customer names the competitor on Stripe. We pull that brand's Ad Library, score creative and copy patterns, and a human checks the read before delivery. Within 24 hours you get a brief-ready PDF with scorecards, hook rankings, and test ideas, plus an ad catalog of every ad asset we found.
- Outcomes
- A shipped product with a simple checkout and a clear deliverable: a brief-ready PDF and an ad catalog your team can use before they write the creative brief.