Data analyst resume example
A strong data analyst resume shows decisions your analysis changed, not dashboards you built. Name the tools — SQL, Python, the BI platform — in a scannable skills block, then use the experience section to connect each analysis to the action it caused and the result that followed.
Summary section
Data analyst with four years in subscription businesses, working in SQL, dbt and Looker. Identified the onboarding step responsible for 34% of first-week churn, and the fix built on that analysis raised 30-day retention by 11 points.
Achievement bullets that work
Written to show the result first and the method second. Adapt the structure — never copy the numbers.
- Identified the onboarding step responsible for 34% of first-week churn through cohort analysis; the resulting fix raised 30-day retention by 11 percentage points.
- Replaced a manually compiled weekly report with a dbt model and Looker dashboard, removing six hours of analyst time per week and eliminating three recurring transcription errors.
- Built the experiment-readout framework now used by all four product squads, standardising how significance is reported and ending a running dispute about whose numbers were right.
- Found and corrected a double-counting error in the revenue pipeline that had overstated quarterly ARR by 4%, then added the test that prevents its recurrence.
- Segmented 180K accounts by usage pattern to support pricing redesign; the tier structure that followed increased average revenue per account by 8%.
ATS keywords for Data Analyst roles
Terms that postings for this role commonly contain. Use the ones that are true of you, in the posting’s own wording.
- data analyst
- SQL
- Python
- data visualization
- Tableau
- Power BI
- Looker
- ETL
- A/B testing
- statistical analysis
- dashboard
- data modeling
- Excel
- stakeholder reporting
- cohort analysis
Report the decision, not the dashboard
The weakest data analyst bullets describe artefacts: "built dashboards in Tableau", "wrote SQL queries", "maintained reporting". Every analyst does these. They describe the medium, not the contribution, and they are indistinguishable from the resume of someone who produced dashboards nobody opened.
The strongest bullets follow the chain from analysis to decision to result: what you found, what changed because of it, and what happened next. "Identified the onboarding step responsible for 34% of first-week churn; the resulting fix raised 30-day retention by 11 points" proves you can find something non-obvious and make it actionable.
When you do not own the outcome
Analysts often influence decisions they do not control, and overclaiming is easy to catch in interview. The honest construction attributes the analysis to you and the outcome to the change: "the resulting fix raised retention by 11 points" rather than "I raised retention by 11 points". Experienced interviewers notice the difference and trust the first more.
Where you genuinely cannot trace an outcome, quantify the work: rows processed, accounts segmented, hours of manual reporting removed, number of teams adopting your framework. Adoption is a real result.
Tools belong in a block, not in prose
ATS matching is literal, and analyst postings are dense with named tools. A skills block listing SQL, Python, dbt, Looker, Tableau and Power BI as plain text matches reliably; the same names scattered through paragraphs may not, and they cost the reader time.
Keep that block above the fold and keep it truthful. Listing a tool you have opened twice is a short path to a technical screen you cannot pass.
Which template suits a data analyst
A clear single-column layout that keeps a dense tools list and detailed outcomes legible side by side.
Questions
- Do I need a portfolio?
- It helps most when your professional experience is thin. A portfolio with two or three analyses that reach a real conclusion beats one with ten notebooks that stop at a chart.
- Should I list Excel?
- Yes. It is still in a large share of postings and is frequently used as an ATS keyword. List it, but never list it first.
- How do I show impact when my work was exploratory?
- Report what the exploration ruled out. "Established that pricing was not the driver of churn, redirecting a planned pricing project" is a genuine, expensive-to-obtain result.