Data concierge · By Gabriel Fernandes

Data Concierge

Your data,
tailored.

I build dbt warehouses, dashboards your team actually trusts, and the governance layer that lets you adopt AI safely.

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Built data at

LeafwellTales Inc.HSTKOutbackVitaconHousiBluefitClubMed

What I do

Four pillars, one commitment: data your team can trust.

Data Warehouse with dbt

Layered modeling, automated tests, living documentation and CI/CD, a foundation that grows with your team, not against it.

  • Dimensional modeling
  • Tests & data contracts
  • dbt docs documentation
  • CI/CD and environments

Dashboards & BI

From metric definition to delivery in Metabase, Power BI or Tableau. One KPI per name, one source of truth.

  • KPI definition
  • Semantic models
  • Executive dashboards
  • Self-service for the team

Governance & AI-readiness

Data catalog (Atlan), metric normalization, and the workflows that prepare your foundation for generative AI on internal data.

  • Data catalog (Atlan)
  • Data contracts & ownership
  • End-to-end lineage
  • LLM/RAG-ready foundation

AI training for companies

Workshops and hands-on tracks that get your team using AI day-to-day with judgment, safety and real outcomes.

  • Tracks for leadership and operations
  • Hands-on with the team's tools
  • Prompts, workflows and guardrails
  • Responsible AI use policy

What it looks like

Dashboards your team trusts.

An interactive preview of a typical dashboard I ship: filters, comparisons, and metrics that hold up under any slice.

Revenue over time

CurrentPrevious period
Range
Segment
8.6%

Revenue

$248.4k

4.2%

Active customers

1,280

0.6%

Data quality

97.4%

0.3%

Freshness

1.4h

47k52k57k63k68k-30d-23d-14d-7dtoday
Illustrative data · For demo purposes only30 days

AI-readiness

Before you plug in AI, fix the foundation.

AI models are only as good as the data feeding them. Companies that rush to ship a copilot without fixing the foundation end up with inconsistent answers, hallucinated metrics, and lost trust. I do the inverse.

Without a trustworthy foundation

  • ✕Duplicated metrics produce contradictory answers.
  • ✕No lineage means no one knows where numbers come from.
  • ✕LLMs hallucinate on poorly defined KPIs.
  • ✕Internal adoption stalls because trust evaporates.

With the base ready

  • →One source of truth per metric.
  • →Catalog + lineage that LLMs can query.
  • →Governance that filters what AI is allowed to see.
  • →Fast adoption because the team trusts the data.
Let's talk about AI + data

How we work

Four steps. No vague promises, no consulting theater.

  1. 01

    Diagnose

    Sessions with the team, a read of the current stack, and a map of what's broken.

  2. 02

    Design

    Proposed architecture, scoped deliverables, success metrics, and timeline.

  3. 03

    Implement

    I build alongside your team, not in silence inside a black box.

  4. 04

    Handoff & training

    Documentation, runbooks, and training so the team can run it without me.

Notes

Writing on data, dbt and AI.

Coming soon

Coming soon

Coming soon

Next step

Let's talk about your situation.

30 minutes, no commitment. You tell me where you are; I tell you if I can help, and if I can't, I'll point you to someone who can.

Book a call