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Data Analyst resume

Data Analyst Resume — Tailored to the Job, ATS-Ready

A data analyst resume has to answer one question fast: what decisions did your analysis actually drive? Recruiters skim for the tools you use, the data you worked with, and the business impact — not descriptions of dashboards you built or queries you ran.

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What makes a strong data analyst resume

1

Business impact — what decision, cost saving, or revenue lift followed from your analysis.

2

Tool fluency — SQL, Python, R, Tableau, Power BI, or whatever the posting names, stated concretely.

3

Data scope — scale of data, domain (finance, marketing, ops), and stakeholders you supported.

4

Methodology — A/B testing, cohort analysis, forecasting, or segmentation you led, not just ran.

Generic AI vs. tailored to the role

Most tools pad a data analyst resume with competence-claims. Resumetion replaces them with concrete facts from your real experience.

Before · generic AI

Detail-oriented data analyst with strong analytical skills and experience working with large datasets to derive actionable insights.

After · Resumetion

Built a churn-prediction model in Python that identified 18% of at-risk accounts 30 days early, enabling outreach that recovered $340k ARR.

Keywords ATS looks for

Applicant tracking systems rank on terminology from the posting. These come up often for data analyst roles — include the ones that match your real experience.

SQLPythonTableauPower BIExcelA/B testingData visualizationStatistical analysisETLLookerBigQueryStakeholder reporting

Templates that suit a data analyst resume

Minimal resume template
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Minimal
Prime resume template
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Prime
Nordic resume template
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Nordic

Data Analyst resume FAQ

Yes, if they show real analysis and a clear finding — not just that you ran a tutorial. A Kaggle project with a concrete takeaway is worth listing; a notebook with no conclusion is not.

Name the database (PostgreSQL, BigQuery, Redshift), the scale (rows, tables, joins), and what the query supported — a dashboard, a report, a model input. "Proficient in SQL" alone tells recruiters nothing.

A data analyst resume emphasises business reporting, dashboards, and stakeholder communication. A data scientist resume leans toward modelling, statistics, and ML pipelines. Match the language of the posting exactly.

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