[Ep.1-2] The Technical Interview That Turned Into a Data Analytics Masterclass

It started with one interview — and turned into my personal analytics lab.

🍋 Section 1: The Interview That Sparked It

It started with a single email — the kind that makes your stomach flip in both directions.

I had landed a technical interview with a national restaurant brand. The role was exactly what I’d been looking for: part marketing strategist, part data analyst, part behavior decoder. The kind of hybrid role I’d quietly hoped existed — and now, I had a shot.

They gave me a case study: three hours to analyze the effectiveness of a marketing campaign using raw data, SQL, and Power BI. I’d just wrapped up a five-month data science bootcamp and had been binge-practicing campaign workflows for weeks. I opened my Notion, rolled up my sleeves — and dove in. And I loved it.

I didn’t get the job.

But the rejection lit a spark. It left me with a question:

What if I kept going?

That case study had revealed something I didn’t know I was looking for — a new way of thinking. A world where marketing wasn’t just instinct or design, but also structure. Where campaigns could be tested, measured, and refined — not just imagined.

I couldn’t let it go. So I didn’t.

Before I knew it, I’d built out a whole ecosystem: behavioral segments, campaign logic, messy data quirks, and just enough chaos to feel real. I drank more matcha than I care to admit.

I generated the data, cleaned the tables, mapped the relationships, and built dashboards in Power BI as if I were running the campaign myself.

If life gave me lemons, I wasn’t just making lemonade — I was naming the flavors, segmenting the drinkers, and tracking their retention curve.

This blog walks you through how I did it — from messy SQL to polished insights, and what I learned along the way.

Table of Contents

1. The Interview That Sparked It

2. I Didn’t Get the Job. So I Built the Job.

3. Designing My Own Dataset (Gemini + Python)

4. PHASE 1: Cleaning Data in SQL

5. PHASE 2: Modeling in Power BI

6. PHASE 3: Creating Relationships

7. PHASE 4: Funnel Analysis

8. PHASE 5: The Story — Visualizing the Insights 

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🍋 Section 2: I Didn’t Get the Job. So I Built the Job.

At first, I told myself this would be a quick project.

That same day of the interview, I came home with the case paper still in my bag — a challenge I couldn’t let go of. I opened my laptop, launched ChatGPT and Gemini, typed in a few prompts, and started building something new. A dataset inspired by the interview — but expanded into a full business scenario: Customers. Transactions. Campaigns. Locations. Even weather patterns.

I planned to build 2–3 dashboards. Just a portfolio piece. A tidy case study to prove I could work with real-world data. But the deeper I went, the more questions I wanted to answer.

What would I need if I were running this campaign from start to finish?

So I designed for mess. For nuance. For behavior.

I built the data to breathe — not just sit in tidy rows.

• Customers with incomplete contact info

• Campaigns with overlapping dates

• Promotions that worked for some segments but not others

  
  

And I realized: this wasn’t just a practice exercise.

This was the job.

Not the title or the offer — but the mindset.

The habit of asking better questions.

The discipline to build systems that could answer them.

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So I kept going.

Over the next two weeks, I built:

• 5 interrelated tables — customers, transactions, products, campaigns, weather

• Dozens of SQL cleaning queries

• A full Power BI model — with calculated measures for revenue, AOV, and campaign ROI

• 10 dashboards — each telling the story from a different lens: executive, marketing, product, analytics

It was thrilling at first. Then frustrating. Then thrilling again.

I redesigned my dashboards. Then redesigned them again. And again.

I forgot what day it was. I caffeinated like a startup founder.

There were moments I felt exhausted.

After all, there was no guarantee any of this would pay off.

But I didn’t quit — because what I was building felt true to how I see the world:

as a series of stories, systems, and subtle patterns waiting to be uncovered.

I didn’t get the job.

But in the process of trying — I built the kind of work I want to keep doing.

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Next: [Ep3]: Designing My Own Dataset (Gemini + Python), we shift gears.

This is where I show you how I did it —from dataset generation, structured the workflow, and designed the logic behind it all.