Services

Seven ways I get hired.

Each engagement below is scoped, priced and delivered on its own. Nothing here is a foot in the door for something larger — if the audit is all you need, the audit is all you buy.

Data stack audit

A data stack audit that tells you what to fix first.

Most teams do not need a rebuild. They need to know which three things are causing eighty percent of the pain, and in what order to fix them. That is what this is.

Your top business-critical metrics reconciled source to model to report, with the exact divergence points named.
For each divergence, a plain verdict: a real business change, or a bug. Stated explicitly so nobody over-reacts to the wrong one.
A ranked list of problems by revenue and risk impact, not by how annoying they feel.

What this involves

Data platform build

A data platform your team can run without me.

The platform is not the point. Being able to answer a new question next quarter without a project is the point. Everything below is built backwards from that.

One warehouse holding modelled, documented, tested data.
Ingestion that recovers from failure on its own and tells you when it cannot.
A transformation layer under version control with tests that run on every change.

What this involves

Analytics engineering

Models and metrics that agree with each other.

When two reports disagree, the problem is almost never the BI tool. It is that the same metric was defined three times, in three places, by three people.

One definition per metric, in code, with the business logic visible.
Tests that fail loudly in the pipeline instead of quietly in a board pack.
Lineage from a dashboard number back to the source column.

What this involves

Product analytics

Tracking you can actually make decisions on.

Bad instrumentation is worse than none: it produces confident answers to questions it was never able to measure. Fixing it starts with deciding what you need to know.

A tracking plan tied to specific decisions, so every event has a reason to exist.
Consistent naming, properties and identity across web, app and warehouse.
Marketing and product numbers that reconcile, with the remaining gap explained.

What this involves

BI and reporting

Reporting that answers the question being asked.

A dashboard nobody opens is not a small failure. It is a running cost, a source of contradictory numbers, and a reason people stop trusting the data team.

A small set of reports mapped to real decisions and real owners.
Metrics that come from the modelled layer, so they cannot silently diverge.
Self-service that works, because the underlying models are readable.

What this involves

AI-assisted data workflows

Put AI where it holds up, and nowhere else.

AI is very good at the parts of data work that are structured, repetitive and reviewable. It is unreliable exactly where correctness matters most. Knowing which is which is the entire skill.

A small number of workflows where AI measurably saves time, and a stated list of where it must not be used.
Quality assessment that any team member can run and get comparable results from.
Documentation that stays current because generating it is part of the pipeline, not a separate task.

What this involves

Fractional data lead

Senior data leadership, part time.

The expensive mistakes in a data function are architectural and organisational, and they get made early — usually by people who were never given the context to decide well.

A data roadmap tied to business priorities, reviewed on a fixed cadence.
Architecture and vendor decisions made deliberately, with the reasoning recorded.
Your existing team supported, reviewed and developed rather than replaced.

What this involves

Not sure which of these you need?

That is the normal starting point. Describe the symptoms and Luís will tell you which one fits — including when the answer is none of them.

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