What Changed Because I Worked On It
I don't measure my work by what I built. I measure it by what became clearer, faster, or more useful because I built it.
The Pattern
Too Much Data → Less Noise
More data was being collected than the client actually needed.
I looked beyond the pipeline and questioned the value of individual fields. I categorized data based on relevance and business priority instead of treating every available field equally.
Reduced unnecessary crawling and shifted the focus toward data that actually mattered to the client.
Dashboard → Decision System
A dashboard can successfully track rankings, competitors, visibility and historical movement — and still fail to answer the question the user actually cares about.
I started looking at the decision behind the metric. For example: if a product drops beyond a certain ranking, what action should the client take?
The focus moved from simply displaying information to understanding how the information could influence advertising, visibility and business decisions.
Data Issue → Root Cause
Recurring data issues were being treated as individual problems.
Instead of only fixing the visible output, I traced the issue backward through the pipeline, QA process, field relevance and assumptions.
The conversation shifted from “Why is today's data wrong?” to “Why does this class of problem keep happening?”
Code → Context
Teams and people change. Systems remain.
Over time, I became familiar not only with how our systems work, but why certain decisions were made, what had already been tried, and what constraints shaped the current architecture.
I became someone who could connect the current problem with the history behind it — reducing repeated investigation and helping teams move faster.
Good engineering solves the problem in front of you. Good systems thinking asks why the problem exists in the first place.
Selected Impact
A few examples of turning data problems into useful systems and decisions.
Competitive Intelligence
Finding the competitors that actually matter.
Built product-level competitor discovery using product names, brands, categories, pricing and similarity signals — see the full case study.
Price Intelligence
Turning marketplace prices into actionable intelligence.
Worked on price intelligence systems that identify meaningful pricing discrepancies across marketplaces while reducing noise from irrelevant data — see the full case study.
Search Intelligence
Turning ranking data into business decisions.
Built search and Share of Search analysis covering keyword rankings, brand visibility, competitor movement and product-level performance — see the full case study.
Data Quality
Reducing noise instead of collecting more data.
Investigated recurring data-quality issues and separated technically detectable fields from information that actually mattered to the client — see the full case study.
Want systems that hold up under real use?
Happy to talk through how a system was built, or why it was built that way.