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Data Analyst Resume Example

The weakest analyst resumes list tools; the strongest list decisions. Every hiring manager already assumes SQL and a BI tool — what separates candidates is evidence that an analysis changed what the business did: a pricing change, a killed feature, a reallocated budget, and what happened after.

Structure each bullet as analysis, then decision, then outcome. "Built a churn model" is a task; "identified the onboarding step where 40% of churned users stalled, prioritized a fix that cut 90-day churn 12%" is a hire. Our bullet guide breaks down the pattern.

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Rendered with the Kernel template — exactly what the PDF export looks like.

Data Analyst bullet points you can adapt

Swap in your own numbers and tools — never paste a bullet you can't back up in an interview.

  • Wrote SQL powering a self-serve metrics layer that cut ad-hoc data requests to the analytics team 35%
  • Sized the revenue impact of a checkout bug within 4 hours of detection, informing the rollback decision
  • Designed cohort analysis showing paid-social users churned 2x faster than organic, shifting $400K of quarterly ad spend
  • Standardized North Star metric definitions across 5 teams, ending week-over-week numbers disputes in exec reviews
  • Built forecasting model (Prophet) for support ticket volume, keeping staffing within 8% of actuals for 3 quarters
  • Audited event tracking before a product launch, catching 14 broken events that would have blinded the readout
  • Presented monthly insights to leadership; two recommendations became roadmap items with measured follow-up
  • Reduced dashboard sprawl from 120 to 40 by usage-auditing and consolidating overlapping reports
  • Documented table lineage and metric logic in dbt docs, cutting onboarding time for new analysts by half

Skills for a Data Analyst resume

Hard skills

  • SQL (BigQuery, Snowflake, Postgres)
  • Python for analysis (pandas, matplotlib)
  • dbt modeling and documentation
  • BI tools (Looker, Tableau, Power BI)
  • A/B testing and statistical inference
  • Funnel, cohort, and retention analysis
  • Data quality auditing
  • Excel and Google Sheets modeling

Soft skills

  • Framing analyses around decisions
  • Saying "the data can't answer that" when true
  • Presenting to executives without jargon
  • Prioritizing ad-hoc requests against roadmap
  • Skepticism about clean-looking numbers

ATS tips for Data Analyst resumes

  • List SQL first and name your warehouses (BigQuery, Snowflake, Redshift) — recruiters filter on the specific warehouse more often than you'd expect.
  • Name both your BI tools with exact spelling ("Power BI", not "PowerBI"; "Looker" vs "Looker Studio" are different products and different filters).
  • Include the statistics vocabulary you actually use — "A/B testing", "statistical significance", "regression" — postings screen for them as keywords.
  • Write dollar or percentage outcomes for at least half your bullets. Parsed text with numbers survives skimming; tool lists don't.
  • "Data analyst" beats creative titles in the headline; if your official title was different ("Insights Associate"), put the searchable term beside it.

Recommended template

Kernel's density fits analysis bullets, a real skills matrix, and education on one page — and its single flow parses exactly through tech-company ATS screens.

See the Kernel template

Frequently asked questions

Data analyst vs data scientist — which title do I target?
Target the work described, not the title: postings labeled "data scientist" that list SQL, dashboards, and experimentation are analyst jobs with inflated titles. Apply to both, but tailor the resume so your top bullets mirror each posting's actual tasks.
Do certificates (Google, Coursera) actually help?
For career changers and juniors, yes — as a legitimacy floor, not a differentiator. One recognized certificate plus a portfolio analysis with real data beats five certificates. After 2+ years of work experience, they drop to a single line.
Should I include a portfolio?
If you're early-career, yes: one or two written analyses (not just notebooks) on public data, showing question, method, decision. Link it in the header as plain text. Employers read the writing quality as hard evidence of the job's core skill.

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