About

01Before data engineering

I spent years running a business before writing my first data pipeline. Inventory planning, cost analysis, sales forecasting, margin optimization, logistics coordination, customer decisions — every day meant working with incomplete information and making the best decision possible with what was available.

That experience taught me something that no bootcamp covers: data is only valuable when it helps someone make a better decision. Everything else is infrastructure looking for a purpose.

When I later learned to build pipelines, write APIs, and train models, I already knew what the output should look like — systems that deliver clarity, not just dashboards that display numbers.

02Today

I design and build production data systems. Pipelines that ingest and transform data from multiple sources. APIs that serve clean, reliable data to downstream consumers. Models that turn historical patterns into predictions. Interfaces that make data actionable for the people who need it.

Every project I ship is shaped by understanding the business context first — what decision needs to improve, who will act on the information, and what quality bar the system must meet to be trusted.

I work across the full stack of data products: from defining the data model and building the ingestion layer to deploying the API and designing the user interface.

03What I'm looking for

Teams where data is a product, not a byproduct. Engineering organizations that invest in data infrastructure, treat pipelines as production systems, and expect their data professionals to understand both the business problem and the technical solution.

I am most effective in environments where I can work across the boundary between business context and technical implementation — understanding what the company needs, and building the systems that deliver it.