Data Analyst · resume examples and templates

Resume for a Data Analyst — what recruiters actually reward

Data-analyst resumes are the noisiest category in tech hiring — everyone claims SQL, Python, Tableau. The ones that get interviews prove ONE end-to-end analysis that changed a business decision. Below is what that actually looks like.

Sections this resume should include

1.Header + LinkedIn + GitHub / portfolio
2.Professional Summary (1 case study in 2 lines)
3.Experience (each role with 1 end-to-end analysis)
4.Skills (grouped: languages, warehouses, BI, stats)
5.Education (+ relevant coursework if new grad)
6.Selected Projects (optional, for portfolio depth)

Keywords ATS filter on

These are the specific terms hiring platforms most commonly filter on for this role. Include them naturally in your bullets and skills section — don't stuff.

SQLPythonTableauA/B testingdbtBigQuerySnowflake

Bullets that work

Two examples of what strong bullets look like for this role — outcome-first, quantified, specific.

Diagnosed a 22% Q3 conversion drop on the mobile checkout flow by joining Snowflake event data with Braze campaign metadata in a dbt model; root cause was a coupon-eligibility edge case affecting first-time buyers on iOS ≥ 17.
Designed and analyzed a 3-variant A/B test on the pricing page (n = 78,000, 21-day runtime); winning variant lifted signup rate 11% (95% CI [8%, 14%]) and was rolled out in week 22.

Bullets that don't

The same information framed as tasks instead of outcomes. Recruiters skim these.

Worked with SQL, Python, and Tableau to analyze business data and create reports.
Built dashboards for various stakeholders and helped with ad-hoc data requests.

Common mistakes to avoid

Tool list without a case study

Every candidate lists SQL / Python / Tableau. The differentiator is naming ONE analysis end-to-end: question → data → method → finding → decision it drove. Without it, you're indistinguishable.

Confusing dashboards with impact

"Built 12 Tableau dashboards" says you can drag fields onto a canvas. "Built the churn-cohort dashboard used weekly by the CS leadership team; drove the retention playbook that reduced 90-day churn 18%" says you drive decisions.

Missing the stack that matters

The warehouse (BigQuery / Snowflake / Redshift) and transform layer (dbt / SQL notebooks) are filter fields. Name them explicitly, don't just say 'cloud data platforms'.

Statistical vagueness

A "significant improvement" without a p-value or effect size reads as marketing copy. "18% lift in 90-day retention, p < 0.01, n = 12,400" reads as an analyst.

Check your resume against this rubric

Paste your existing resume into our free ATS Checker and see which of the 8 rubric items you're passing and which you're not.

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