Data Analyst resume guide

This guide helps you tailor a Data Analyst resume to a specific job description while keeping it clear, truthful, and ATS-friendly.

What hiring teams look for

• Turning data into decisions (insights, recommendations)
• Stakeholder management and communication
• Dashboards and KPI ownership
• Analytical rigor (segmentation, cohorting, experimentation)

Strong resume structure

• Header (name, location, links)
• 2–3 line summary aligned to the role
• Skills (grouped, not a keyword dump)
• Experience (impact-first bullets)
• Projects (optional but powerful)
• Education / certifications (as relevant)

Skills section: what to include

• Core: SQL, spreadsheets
• BI: Looker/Tableau/Power BI (what you used)
• Analytics: funnels, cohorts, retention, A/B tests
• Data quality: definitions, metric consistency
• Optional: Python/R if you used it

Bullet writing: the formula that works

Use: Action + Method + Result (+ Scope)

Examples:

• “Built a KPI dashboard used by 6 teams, reducing weekly reporting time by ~70%.”
• “Defined churn metrics and cohort analyses that informed pricing changes and improved retention by 4%.”
• “Partnered with Growth to evaluate experiments, standardizing A/B test readouts and decision criteria.”

ATS and formatting notes

• Make dashboard and analysis work easy to read (avoid wall-of-text)
• Use clear KPI names and outcomes, not only tools

Common pitfalls

• Over-focusing on tools with no business outcomes
• Vague claims like “data-driven insights” without examples
• Missing stakeholder context (who used the analysis?)

Using HyperApply for this role

• Use HyperApply to mirror the job’s KPI and stakeholder language in your summary and top bullets.
• Related: /learn/how-to-write-a-resume-summary-that-matches-the-job