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Data Analyst · CV-eksempler og maler

CV for Data Analyst — det rekrutterere faktisk belønner

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.

Seksjoner denne CV-en bør inneholde

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

Nøkkelord ATS filtrerer på

Dette er de konkrete termene ansettelsesplattformer oftest filtrerer på for denne stillingen. Bruk dem naturlig i kulepunktene og ferdighetsseksjonen — ikke stapp.

SQLPythonTableauA/B testingdbtBigQuerySnowflake

Kulepunkter som fungerer

To eksempler på sterke kulepunkter for denne stillingen — resultat først, tallfestet, konkret.

  • 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.

Kulepunkter som ikke fungerer

Samme informasjon formulert som oppgaver i stedet for resultater. Rekrutterere skummer over disse.

  • 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.

Vanlige feil å unngå

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.

Sjekk CV-en din mot denne rubrikken

Lim inn den eksisterende CV-en i den gratis ATS-sjekken og se hvilke av de 8 rubrikkpunktene du består — og hvilke du ikke gjør.

Kjør ATS-sjekken

Få hvert kulepunkt omskrevet for denne stillingen

Emplirao-prøven omskriver svake kulepunkter til stillingsspesifikke resultater med samme stemme som eksemplene over — og matcher deretter CV-en mot aktive utlysninger i faget ditt.

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