Staff Data Engineer · Curitiba, Brazil
Leon Matheus
Data is shared meaning. I work on the structures that keep it alive: architecture, modeling and governance for organizations that need to remember.
What I keep coming back to
Data engineering is about building traps.
Not tools, not just data: abstractions of meaning that let an organization actually perceive what it does.
Oriented to data, not only by data.
When AI agents become the connective tissue between systems, what matters is that data is understood by people, agents and machines.
Fundamentals outlast waves.
Twenty years of hype taught me that concepts are more portable than any tool that implements them.
Latest writing
- A machine that doesn't know how to remember
The enterprise produces knowledge all day and forgets what it wanted to know. A data model is the minimum shared truth that lets it remember.
- There is no generic data
Becoming data-driven is not trading intuition for KPIs. Data always has a shape, a source and a life cycle, and it takes a culture to keep it meaningful.
- The mystery of knowledge
We've invested like never before in computing to extract knowledge from raw data. These technologies seem to demand that we think better, not less.
- Oriented to data, not only by data
Databases are no longer just backing services. With AI agents as connective tissue, modeling moves up to the layer of meaning.
- Fundamentals outlast waves
Twenty years of innovation and hype in analytics. Fundamental ideas last longer and travel further than any tool.
- Modeling is not architecture
The medallion architecture organizes a pipeline into layers. It says nothing about how to model the information.