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Data Analyst resume example

Turns raw data into decisions the business actually makes. Below is a complete data analyst resume, written the way one has to be written to survive a first screen, with every bullet explained underneath.

199
open right now
118
companies hiring right now
27%
of those openings are remote
Mostly in

New York, San Francisco, Berlin

Hiring now
  • Guidehouse21
  • Preply7
  • Revolut7
  • Toss7
  • Lyft5
  • Stripe5
  • Flix4
  • FREENOW4

Counted by Refolk from the public job boards of 1,745 companies, refreshed through the day. These are live openings, not a forecast, so the numbers move as roles are posted and filled.

Alex Moreno

Data Analyst

alex.moreno@example.com · +1 555 0134 · Berlin · alexmoreno.example

Summary

Data Analyst with eight years of experience. Found that 30% of churn traced to one onboarding step; the fix cut 90-day churn by 4 points. Looking for a data analyst role with more ownership of SQL and the decisions around it.

Experience

Senior Data Analyst, Northwind Systems

2022 - Present

  • Found that 30% of churn traced to one onboarding step; the fix cut 90-day churn by 4 points.
  • Automated the weekly leadership report, removing 12 hours of manual spreadsheet work a month.
  • Built the experiment analysis framework the product team now uses for every test.

Data Analyst, Meridian Labs

2018 - 2022

  • Owned the Python side of the work at a company with a six-person data team, and documented it well enough to hand over cleanly.
  • Set the approach the team still uses for dbt.

Skills

SQL · Python · dbt · Tableau · Looker · Excel · Experiment analysis

Education

BSc Statistics, 2018

A sample, not a real person. The name, the employers, and the figures are invented to show the shape of a strong page - use the structure and put your own evidence in it.

How to read this data analyst resume example

The sample above is not a template to copy word for word - copied resumes read as copied. It is here to show the shape of a page that gets past a first screen: one column, standard headings, and bullets that end in an outcome rather than a duty. Decisions that changed because of your analysis, not dashboards you built.

  • Every bullet opens with a verb and carries a number. That is the pattern, not a coincidence.
  • The summary makes a claim and then supports it in the first bullet underneath.
  • Skills are named tools, not adjectives, and each one appears again in the experience section.
  • The earlier role is short. Recent work carries the weight of the page.

The numbers in a data analyst resume

The most common thing missing from a data analyst resume is a number. Not because the work had none, but because nobody wrote them down at the time. These are the measures a data analyst can usually reach for, and the example uses them.

  • Found that 30% of churn traced to one onboarding step; the fix cut 90-day churn by 4 points.
  • Automated the weekly leadership report, removing 12 hours of manual spreadsheet work a month.
  • Built the experiment analysis framework the product team now uses for every test.

Before and after: rewriting a weak bullet

Most data analyst resumes are one edit away from being much stronger, and the edit is the same every time: replace the description of the job with the result of doing it. The pairs below are the same work, written twice.

  • Weak: "Responsible for sql and related tasks." Strong: "Found that 30% of churn traced to one onboarding step; the fix cut 90-day churn by 4 points."
  • Weak: "Worked on python projects across the team." Strong: "Automated the weekly leadership report, removing 12 hours of manual spreadsheet work a month."
  • Weak: "Helped improve processes and supported data analyst initiatives." Strong: "Built the experiment analysis framework the product team now uses for every test."

Adapting the example to your own history

Work backwards from the posting. Find the two or three things it actually screens for, then make sure the top third of your page answers them. Everything below that is supporting evidence.

  • Reorder your bullets so the one closest to the posting comes first in each role.
  • Rewrite the summary for the specific job. It is the only part a human reliably reads.
  • Cut any skill you would not want to be asked about for ten minutes.
  • Keep SQL, Python, dbt visible in context, not stranded in a list.

FAQ

Can I copy this data analyst resume example?
Copy the structure, not the sentences. The layout, the ordering, and the shape of the bullets all transfer. The content does not: a recruiter who reads twenty resumes for the same role notices identical phrasing quickly, and the numbers in this sample are illustrative rather than anyone's real history.
What if I do not have numbers like these?
Most people have more than they think. Look for volume, frequency, time, cost, error rate, or a before-and-after on anything you touched. Where the number genuinely does not exist, say what changed and who noticed - "the process that used to need a weekly meeting now does not" is a real outcome without a metric.
How long should a data analyst resume be?
One page up to about eight years of experience, two pages beyond it. This example is one page. Length signals scope, so a long page with small-scope bullets reads worse than a short one.
Will this format pass an applicant tracking system?
Yes. One column, real text, standard headings, and no graphics or tables is the format parsers handle reliably. Most parsing failures come from multi-column layouts, text inside images, and headings the parser does not recognise.
Should the example change for a more senior data analyst role?
The structure stays. What changes is the scope in the bullets: more ambiguity you resolved yourself, more decisions with a tradeoff, and more effect on work that was not directly yours. Decisions that changed because of your analysis, not dashboards you built.

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Put this to work

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