I sat down with my younger brother, Graham, last week to help him with his job search.

He wants to land his first data job, so we reviewed his resume, LinkedIn, and portfolio.

He already had good things to work with. He’s fairly comfortable with tools like Tableau and Python. He even had GitHub repos with some analysis he’s done previously.

The problem was that none of it was easy to understand.

A recruiter would probably look at his stuff for 10 seconds and move one.

Being qualified is not enough. You have to look qualified. And look qualified fast.

We fixed his biggest mistakes in about 30 minutes.

Here are the 4 biggest changes we made. If you want to get hired, steal these.

1. Your Title Should Help You

On his resume, Graham called himself a "Junior Data Scientist." On LinkedIn, his headline said "Aspiring Data Analyst.”

His first impression was giving “I’m not really qualified”, even if he is.

“Junior” tells the hiring manager your level before they get to decide. "Aspiring" tells them you're not even there yet.

A data analyst is someone who analyzes data. If you’ve analyzed data before, call yourself a Data Analyst.

Let the hiring manager decide how experienced you are. That’s their job. Do not do it for them.

We also audited his titles for previous jobs. Cleaned them up. Tried to make them appear more “data analyst”y.

It’s important to do this to get past the ATS (the software that filters resumes before a human sees them).

Of course, don't lie. But also, don’t cut yourself short.

If you pulled reports, tracked trends, or used Excel to make decisions, that's real analytical work. Own it.

2. Show The Result, Not Just The Task

Next, we looked at his resume bullets.

Graham had one that said: "Integrated statistical analysis into engaging audience-friendly media."

A bullet point without numbers is just a list of chores. You have to show how much you did and why it mattered.

My question was, so what? What did that actually do?

I asked him how many pieces of media he made.

"About 100."

How many views did they get?

"Maybe 100k."

We rewrote it: "Performed statistical analysis on 100+ pieces of engaging media that generated 100,000+ views."

Instantly better. It shows scale, impact.

Look at your own resume. For every bullet, ask two questions:

1. "How much?" (How big was the data set? How many reports did you make per week?)

2. "So what?" (Did your work save the company money? Did it save your team five hours a week?)

If you do not know the exact number, use a safe, honest guess. Just make sure you can explain that number in an interview.

3. Stop Sending Recruiters Raw Code

This was the biggest one.

Graham had solid GitHub repos. But if you clicked the link, you just saw a wall of Python files.

No hiring manager is reading that. They don't want your raw code. They want the story.

This is always why I’ve said, “GITHUB IS NOT A PORTFOLIO.”

We dropped his GitHub links into a portfolio builder (MyDataFolio), and it auto-generated a case study in two minutes.

Suddenly, his project showed the problem, the tools he used, the main findings, and a big visual at the top.

A recruiter who does not code could look at it and understand the project fast.

Or at least see it looked cool and impressive.

And sometimes, that’s enough.

4. Fix The Small Things Too

Finally, we cleaned up his LinkedIn. People ignore the small details, but they matter.

Graham’s profile picture was a casual selfie with a kitchen sink in the background. We talked about swapping it for a clean headshot.

His work experience section had job titles but zero bullet points. Recruiters often check LinkedIn before they open your resume. An empty experience section looks like an incomplete profile. We copy-pasted his new resume bullets straight into LinkedIn.

We also added tools like “Python,” “Excel,” and “Power BI” to his About section so recruiters could find him more easily. Keyword stuffing. It works.

The Trick to Landing YOUR First Data Job

Before you learn another coding language, fix how you present the skills you already have.

Look at your resume and LinkedIn right now. Do you look like a messy, unorganized, hobbyist or do you look like a grounded, professional who will bring value to a company?

If it’s not the second, spend less time on learning skills. More time on showcasing them.

Chances are, you’re skilled enough. Just no one knows it.

Make it easy for them to hire you.

And if you want help with this trick, I'm here to help.

This is the EXACT kind of audit we do inside Data Analytics Accelerator. We review resumes, LinkedIn profiles, and portfolios, just like I did with Graham.