Most business users can't access their own data. The marketing analyst who needs last month's regional numbers has to file a ticket and wait two days. The ops manager tracking deal velocity is working from a spreadsheet that's three weeks old. The business owner who wants to understand their top customers has no idea where to even start.
Golden Analytics is building toward "Canva for data" — a world where any business user can understand, explore, and act on their data, without needing an engineer, a BI tool license, or a SQL degree. The people we're building for are not technical. They are smart, they have real questions, and they are currently stuck.
You're joining the team. Your first challenge is also the most important one we work on every day: how do you make data genuinely useful for someone who has never written a query?
You have 30 minutes and a real dataset to work with: Washington State fiscal data (link to data set below as well) — public government spending broken down by fund type and fiscal year. This is real data, with real shape and real quirks. You do not need a database connection; you can embed the data directly or load it from the file.
Your task: Propose one way to help a non-technical user get value from this data. Then build it as a proof-of-concept web app.
The solution is yours to design. We want to see your product instincts alongside your engineering. What do you think would genuinely help someone who has never looked at a government budget? A city councilmember trying to understand where the money went. A journalist tracking spending trends. A policy analyst who knows the questions but not the SQL. Build something for one of them.
Every solution — whatever direction you take — must satisfy these two things:
AI usage: Golden Analytics builds AI-native features — it's a core part of what this role does. If your solution includes an AI or intelligent component, great. If it doesn't, explain in your README why you made that choice and where you see AI adding value in a future iteration. Either approach is valid. What we're looking for is intentional product thinking, not checkbox compliance.
If you use AI/ML components: All inputs to any model, vector store, or intelligent component must be logged — even in the POC. You do not need a persistent log store, but your code must show clearly where and how logging would happen. This is a governance requirement in enterprise B2B contexts.
This is a proof-of-concept. You are not expected to ship production code in 30 minutes. What we're evaluating is your thinking, your trade-offs, and how you approach the problem — not polish.
We expect you to use AI tools. We evaluate how you use them — not whether you use them. Evidence of iteration, redirection, and critical evaluation scores higher than a polished output with no process documentation.
The single highest-signal indicator: your video answer to the mandatory AI question. If you cannot name a specific moment where you redirected AI output, evaluators will assume you did not.
Mandatory AI question (answer in your video):
Walk me through one moment where you disagreed with, pushed back on, or redirected what the AI gave you — and what you did instead. Name the specific moment. Explain what the AI produced that didn't meet the bar, what you did differently, and why.
Product instincts for designing data tools for non-technical users — translating the "Canva for data" vision into a concrete implementation decision
Ability to build a working web app proof-of-concept under a real time constraint, with explicit reasoning about what you built and what you deferred
Awareness of the gap between a proof-of-concept and production software — what would need to change before this could ship
Genuine AI collaboration — using AI as a thinking partner with documented process, not as a code generator
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