About
I'm a Data Engineer with a background in Mathematics, focused on building reliable, business-facing data products.
I like turning messy problems into clean data models, robust pipelines, and analytics systems people can actually trust.
My work sits around analytics engineering, modern data stacks, and thoughtful system design, especially where technical depth meets real business usefulness.
What I care about
End-to-end ownership
I like owning the full path from ingestion to business consumption, not just one isolated part of the pipeline.
Governed analytics
I care about semantic layers, metric consistency, and data models that make self-service analytics actually sustainable.
Automation where it matters
I use Python, APIs, orchestration, and CI/CD to reduce manual effort and make data workflows more repeatable.
How I work
I try to balance engineering quality with business usefulness. That means thinking carefully about data models, operational simplicity, reporting needs, and how a team will actually consume the output once the pipeline is live.
I've shipped dashboards and data products in Power BI, Looker, and Lightdash, and I enjoy the craft of defining governed metrics and semantic layers that help teams move faster without losing trust in the numbers.
Outside engineering
Outside engineering, I'm mostly into music, economics, literature, and building things. In books I lean more toward philosophy and essays than business non-fiction. I'm also interested in product, strategy, and how technical work connects to business.