About Me
The Story
I started as a Data Analyst, building dashboards and helping teams make sense of their data. That gave me a deep appreciation for clean data and clear metrics. But I kept hitting the same wall: dashboards are only as good as the data feeding them.
That pushed me into data engineering — building the pipelines, managing quality, and designing infrastructure that lets analytics actually scale. I spent time learning the architectural decisions that separate “it works” from “it works reliably under pressure.”
More recently, I’ve been focused on the analytics engineering side of the stack — owning the full journey from source systems through modelling to the dashboards people rely on every day. That’s where the real value lives: in the last mile between raw data and a confident decision.
What ties it all together: I like turning ambiguity into systems. Messy data into reliable pipelines. Fragmented reporting into a single source of truth. Scattered requests into self-serve reporting. That’s the work I do best and enjoy most.
What I Bring
I approach every engagement the same way: build things people trust, use, and understand. Four principles guide how I work.
Reliable data foundations
I build pipelines, quality checks, and models that hold up under real-world pressure — so teams decide on data they can trust, not data that breaks on a Friday afternoon.
Analytics people actually use
I design dashboards and metrics around how teams genuinely work, not around what is convenient to build. If it sits unused, it was a wasted effort.
Insights that drive action
I care about what happens after someone looks at a chart — clearer KPIs, one source of truth, and answers that turn into decisions rather than more questions.
A pragmatic operator mindset
I started as the analyst stuck with messy spreadsheets, so I value practices that keep things simple, documented, and maintainable — even when no one is watching.
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