What I work on
My professional work sits where platform architecture meets delivery: lakehouse systems, data quality, governance, lineage, access controls, infrastructure as code, and the pipelines that make those foundations useful.
I have delivered systems across ERP transformation, multi-country finance analytics, platform modernization, and operational decision support. The recurring goal is simple: make complex data dependable enough for teams to use and change safely.
How I approach the work
Governance belongs in delivery
Quality checks, ownership, access policy, lineage, and reproducible infrastructure are part of the product—not documentation added after launch.
Outcomes need evidence
I describe work through the system built, the constraints handled, and measurable results where they are available. The same principle guides my AI Lab: evidence before fluency.
Architecture should survive change
I prefer explicit contracts, observable pipelines, and shared models that let teams modernize without breaking the operations that already depend on them.
Current focus
I’m interested in cloud data engineering roles, platform delivery, and serious conversations about governed data systems. Alongside professional work, I publish practical notes and build public-interest AI experiments such as MonÉlu.