How to write a data analyst resume
A strong data analyst resume puts SQL front and center, then proves every tool with a decision it drove — the pattern is "analysis → decision → number" (e.g. "Built the churn cohort analysis in SQL + Tableau that led ops to cut onboarding steps, lifting week-4 retention 9%"). List SQL, Excel, Python, and your BI tool (Tableau, Power BI, or Looker) in Skills, and keep it to one page.
What recruiters and ATS look for in a data analyst resume
Recruiters filter data analyst resumes on SQL first — it appears in nearly every JD — then on the specific BI tool their team runs. The differentiator is whether your dashboards changed anything: an analyst who lists "built 15 dashboards" reads as a report factory, while "the pricing dashboard that moved the team to usage-based billing" reads as an analyst worth interviewing. End bullets in decisions and business numbers, not deliverables.
Section order: Summary → Experience → Skills (grouped: SQL & languages / BI tools / Methods) → Projects → Education. New grads: lead with Projects.
ATS keywords for a data analyst resume
These are the keywords most data analyst job descriptions use as ATS-filter inputs. Include the ones you genuinely have evidence for in your Skills section.
Starter Skills section
A starting point for your Skills section. Prune to what you genuinely have evidence for.
Best action verbs for data analyst bullets
Lead every bullet with a strong, specific verb. For this role, the strongest openers are:
Example bullet points (before → after)
Three rewrites following the action-verb / quantified-outcome pattern. Replace the specifics with your own. Never invent numbers.
Data Analyst resume FAQ
SQL is non-negotiable and should appear in both Skills and your bullets. Add Excel, one BI tool named exactly (Tableau, Power BI, or Looker), Python or R, and method keywords like A/B testing, cohort analysis, and forecasting. Group them so a recruiter can scan in seconds.
Increasingly yes for mid-level roles, but SQL matters more. If you know basic Python (pandas, matplotlib, scheduled scripts), list it with evidence of one real use — automation of a recurring report is the most credible.
A data analyst resume emphasizes SQL, BI dashboards, and decision support; a data scientist resume emphasizes statistical modeling, machine learning, and experimentation infrastructure. Titles overlap, but ATS keyword sets differ — match whichever the JD actually asks for.
Yes if you're early-career: 2-3 projects with real datasets, a clear question, and a stated decision or insight beat any course certificate. Link one canonical GitHub or portfolio URL.
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