Are you applying to data jobs and getting nothing but rejection? That sucks. But it doesn't mean you're a candidate.

In fact, you could still be a very good candidate. But there's just one big problem.

Your resume sucks.

This week, I watched a recruiter review data analyst resume on camera.

It was sobering. This was a good Data Analyst candidate. They had over 5 years of experience w/ a lot of skills. And yet, the recruiter hated their resume - barely even considered it. No interview.

Here's why the recruiter hated (and how you can fix your resume):

(p.s. the easiest solution is to just use a good template to start with. I'll just give you the resumeI give to my DAA students - you're welcome).

Problem 1: The resume was unscannable

Recruiters have 20 seconds to scan your resume.

This resume was way too word-dense. The professional summary ran eight lines straight with no breaks. Then it went right into a core skills section that was just keyword stuffing. Then the bullet points in the experience section were all two to three lines each.

Woof. Hard to scan quickly.

It was just block text everywhere. Lots of information, but none of it digestible in those 20 seconds.

The recruiter literally started whiting out sections on screen. "Doesn't matter. Doesn't matter. Doesn't matter. Doesn't matter."

He went through the entire resume that way. By the time he got to the one bullet point that might have been decent, he'd already given up.

The harder you make a recruiter work to find the gold in your resume, the worse your chances get. Exponentially.

Recruiters want children's books - not novels.

Problem 2: Plenty of keywords, but no qualifications

The resume had a skills section loaded with what the recruiter was looking for: Power BI, Excel, SQL, R, Python, Databricks, AWS.

But he said he's not keyword hunting — he's qualification hunting.

What's the difference?

Keyword = a skill.

A qualification is a keyword plus where you used it, plus how you used it, plus what purpose you used it for.

Keyword: SQL

Qualification: "Used SQL to analyze 400,000 rows of data and save $10,000 in costs at a Fortune 500 company."

That's the keyword (SQL), the where (Fortune 500 company), the how (queries on 400K rows), and the why ($10K saved).

Anyone can list SQL on a resume. But not everyone tells how, where, why, and when they used it.

Problem 3: Not enough business value

One of the bullet points on the resume said the candidate "conducted a deep analysis of 65 legacy ETL pipelines, reverse engineering undocumented business rules and transformation logic across 9,000 processes."

The recruiter's response? "No, no, no, no, no, no, no, no!"

He said that's too technical. He doesn't care that you did it — he wants to know why you did it. What was the result?

Your job as a data analyst is to make money or save money. The recruiter wants to see that. He said he needs you to meet him in the middle between "I analyzed data" and "the company made money."

Another bullet said the candidate "designed and built an executive reporting dashboard tracking pipeline health." But WHY did you do that? Was it for fun? Was it to save the company $1? Or was it to save the company $1,000,000? Did that analysis save time? Was it 15 minutes saved? Or 15 weeks saved?

Specific dollar amounts. Specific percentages. Specific tools. That's what gets you past the 20-second scan.

This resume process sucks

It sucks that you get judged as a complete human being — all your capabilities, all your skills — by a white page with black text on it in 20 seconds. Yup. I agree.

But that's the game we have to play.

You can either play it well or stay stuck. Giving up sucks too. Both options are hard. Just different hard.

Choose your hard, I guess.

If you want the easy way out, I built a data analyst resume template with all this structure already baked in. You can grab it for free at datacareerjumpstart.com/resume.

Hope it helps!