Data consulting

Most data problems don't start in the report.
They start at the source.

I work the data end to end: from how it is collected, to the engineering that moves and joins it, to the analysis that turns it into a decision. I don't stop at consulting: I create the solution and implement the decisions we make together.

Experience

More than ten years in data and analytics. I started in tracking and web analytics, and over time the work expanded into data engineering, warehousing, analysis and privacy. The through-line never changed: make the data trustworthy at the source, so every decision on top of it can be too.

I started as an analytics intern at an agency in São Paulo and became a data analyst there. In 2017 I moved to Dublin, where I spent almost seven years at a digital agency, going from analyst to Director and Partner of Data and Analytics. I left in 2023, took a sabbatical year, and founded BRLabs in 2025.

I am not only a consultant. When a problem calls for it, I build: I created a framework for an entire discipline and wrote the platform that runs it. I also read and write code, so I can audit how something was actually implemented, point to where it is wrong, and support the team that has to fix it.

Degree in Systems Analysis and Development from the Instituto Federal de São Paulo. Based in Buenos Aires, working with clients in Brazil, Europe and the US, in English, Portuguese and Spanish.

What I do
05

Clients almost always arrive with the same symptom: numbers that don't match, conversions that vanish, reports that never add up. And the team spends a quarter treating the symptom and making decisions on data nobody trusts. Almost always the cause sits before the report. So my work runs from the question to the decision: figuring out what the business needs to measure and how to measure it, implementing it, and delivering the report or dashboard that actually guides the decision, from attribution to cohort and lifetime value. The four capabilities below are what hold that arc up.

// the risk

Data protection laws turned undisciplined collection into legal exposure, not just a reporting problem.

// what's coming

AI models decide from what you collect. Sloppy collection corrupts every layer after it, and now the decisions are automatic.

Architecture from the source

Deciding what gets collected, under which conditions and in what shape, so the data is correct at birth and the person, not the session, becomes the unit of analysis. This is the layer everything else rests on: get it wrong here and no report downstream can be trusted.

collection identity specification
Data that is yours

Getting information out of the tools that rent it to you and into infrastructure you own, with reliable pipelines that run on their own and warn you when they break. Data that fails silently is what takes a report down on a Monday morning, so the pipeline is built to be watched, not just to run.

data engineering pipelines reliability
Integrity and trust

Investigating where the numbers disagree and why, then making the data verifiable and documented, so the team can trust it. The goal is not a one-off fix, it is data your people can stand behind.

audit verification documentation
Lawful by design

Treating privacy and governance as part of the architecture, not a patch at the end. Knowing what you collect and why, on a lawful basis, handling consent and international transfers, and acting as Data Protection Officer when the project calls for it. Most of the real risk sits at collection, which is exactly where I already work.

LGPD GDPR DPO
A framework of my own

After repeating the same diagnosis across many projects, I systematised it into a framework of my own for data integrity at the source, and I built the tool that makes it real. The core move is to treat measurement as a contract: specify what should be collected, under which conditions and in what shape, then watch the live data against that contract and raise the alert when something drifts, before the client notices.

The tool that runs the framework is Martex, a platform I designed, coded and maintain. Blueprint writes the contract, a versioned document you can hand to a developer. Pulse then watches the live data against it and fires the alert when an event stops firing or a value drifts.

The upstream data integrity framework Five steps in three phases. Define how it is, define what you need, implement the spec, validate what you implemented, validate the integrity. If step four fails it returns to step two; step five runs continuously and returns to step one when the real world changes. Define Implement Validate 01 02 03 04 05 Define how it is Define what you need Implement the spec Validate what you implemented Validate the integrity on failure, revise the contract continuous, re-assess the current state 01 02 03 04 05 Define how it is Define what you need Implement the spec Validate what you implemented Validate the integrity on failure continuous

It is not a cascade. Validation that fails sends you back, and step five never stops running.

12 tools · free tier for up to 3 clients · 7-day PRO trial
try it free ↗ read the framework ↗
Tools
Collection & tagging
Google Analytics (GA4)Google Tag Managerserver-side taggingtag gatewayConsent Mode v2
Data engineering & cloud
BigQueryGoogle CloudCloud RunCloud FunctionsCloud SchedulerETLSQLPythonMySQLAPIs / REST
BI & reporting
Looker Studiodashboardsdata modellingattributionLTV / CACcohort analysis
Privacy & compliance
LGPDGDPRCMPconsentcookiesDPO

Consultants who only recommend are everywhere. I conceive the method and deliver the system that makes it real.

This is one arm of BRLabs, the same place where I build products and research. See BRLabs ↗

Get in touch

Messages go straight to my inbox. I schedule calls manually rather than through a booking link, so tell me what you need and I'll come back with times.

brlabsgroup@gmail.com ↗ LinkedIn ↗