For $50M+ DTC eCom
Fractional Head of Analytics
Two-week DAR loops (Diagnose, Act, Reflect) anchored to the Horizon. The 9am dashboard the CMO trusts. Definitions every department signs off on. Production and dev environments aligned. The foundation that earns AI.
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The premise
Most marketing data problems aren’t accuracy problems. They’re governance problems disguised as data-quality problems. Buying a new tool doesn’t fix governance. What does: visible lineage, smaller iterations, and difficult conversations brought to a foundation everyone can see.
Rigid frameworks don’t fit your company. Iteration with strategic intent does.
How DAR loops work
Diagnose
What’s actually broken, with evidence. Lineage maps. Definition audits. The places marketing, finance, and operations have been quietly disagreeing.
Act
The smallest unit that ships value. One artifact, end-to-end, on the shared foundation. Production and dev aligned. No drift.
Reflect
Stakeholder review every two weeks. What worked. What didn’t. What changes for the next loop. The Horizon stays the anchor.
What you walk away with
Month 1
- The Horizon defined and signed off by stakeholders
- A bird’s-eye view of your data infrastructure, models, and lineage
- The first trusted artifact shipped on the new shared foundation
Month 2
- Deduplicated mappings and calculations across the warehouse
- Production and dev environments aligned, no drift
- Automated 9am dashboards on shared models for marketing, finance, CX, and operations
- A measurable cost reduction from retiring duplicated pipelines
Month 3+
- Definitions every department signs off on, written into the models, not the documentation
- The foundation that earns AI: agents and RAG deployed only where the data supports them
- A team that owns the system after I leave
“Lluis came in to help us with dashboards and within a month told us that we didn’t have a problem with the dashboards. The problem was the governance. Three months later we had a clean dbt foundation, revenue and margin definitions every department had signed off on, automated 9am reporting running on shared models, and a 66% reduction in data processing costs. The dbt work was the easy part. The hardest part was the conversations he made us have. Worth it.”
AI on a foundation that earns it
AI doesn’t fix your data problems. It amplifies them. Garbage in, garbage out, on steroids. Adoption-first earns the right to deploy AI without amplifying the mess underneath.
I have skin in your outcome, not in any vendor’s revenue. No preferred partners. No reseller margins. If the right answer is your existing BigQuery + dbt, that’s the answer. If it’s sunsetting a $200K platform, that’s the answer.
Engagement
$1,250 per day. Retainer-based, billed monthly.
Engagements are sized in days per week:
- 1 day/week — $5,000/month
- 2 days/week — $10,000/month
- 3 days/week — $15,000/month
Two-week sprint cadence with stakeholder review at every cycle. Most engagements run 1–3 days per week over three to nine months, until your team owns the system.
Most engagements start with a 4-week First Loop, which scopes the ongoing retainer against your real stack — not a deck.
Recent work
A $100M+ DTC eCom: a 9am dashboard the whole org trusts, definitions every department signed off on, 66% reduction in data processing costs. Two months. No new tool.
Start with The First Loop
Most engagements start with a 4-week First Loop. It scopes the retainer against your real stack, not a deck.