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Illustrative example · fictional profile, real scoring rubric

Data analyst example

69
out of 100
Resume Readiness Score
Medium confidence · 3 repos · 3 READMEs

Nadia, here's what we found

Nadia, the profile shows the full analyst arc rather than one slice of it: exploratory SQL and notebook work, modeled data with dbt, and dashboards that put the results in front of someone.

The dbt models are what lift this above a folder of one-off notebooks.

86
Project Quality
40
Technical Breadth
100
Documentation
33
Engineering Practices
90
Career Presentation
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Standout project

retail-insights

The one to lead your resume with. It checks every box a recruiter skims for.

Substantial codebaseWell documentedEngineering practices visible (tests or CI)External adoption or collaboratorsActively maintained

Top repositories

dbt-sales★★★documented · CI
viz-gallery★★★documented · deployable

Read like a code review

+Your deployment experience is visible: containerized or infrastructure-defined projects read as production-minded.
+Most of your top projects are documented, which is rarer than it should be.
+Recent, maintained work: your most important repositories show current activity.
+Automated pipelines show up in your work, a strong engineering-practice signal.
→Presentation looks solid; the next lever is measurable impact statements in each README.

Strongest languages

Jupyter Notebook
64%
Python
36%

Detected stack

Data & infrastructure
Docker
CI/CD
GitHub Actions

Evidence used

Everything above is computed from this. Nothing else.

3repos analyzed
3READMEs
1Dockerfiles
1CI workflows
38stars
96 KBof code
What a recruiter probably sees
"An analyst who is comfortable from raw SQL through analytics engineering to a shipped dashboard, which is a wider range than most portfolios show."

Based on the public repositories analyzed, a recruiter is likely to infer an analyst who is comfortable from raw SQL through analytics engineering to a shipped dashboard, which is a wider range than most portfolios show. Stating the actual finding from retail-insights in its README would demonstrate the 'clear answers' part directly.

@pivot-and-prove · analyzed Jul 20, 2026zelume.io/github-resume

Recruiter visibility

40%

of expected data-ml signals present · heuristic

Missing: An ML/data framework, Notebooks or experiments, A database or data pipeline

Drafted from the evidence

Written only from the repository evidence. No invented numbers.

retail-insightsAnalyzed a retail sales dataset with SQL and pandas, documenting the findings in a reproducible notebook
dbt-salesBuilt dbt models transforming raw orders into a clean sales data mart, tested in CI

Where it lands in the editor

Nadia Rahman
github.com/pivot-and-prove
Projects
  • Analyzed a retail sales dataset with SQL and pandas, documenting the findings in a reproducible notebook
  • Built dbt models transforming raw orders into a clean sales data mart, tested in CI
Skills
Jupyter Notebook, Python, Docker, GitHub Actions
Experience
  • Software Engineering Intern, Freshbooks (Summer 2025)

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