Data Analyst résumé example for 2026
A data analyst résumé has to prove two things a job description never says outright: that you can get the number, and that someone changed a decision because of it. This example leads with the decision.
Opens in the editor as your own document. No account, and it never leaves your browser.
What a data analyst résumé has to show
- SQL first in the skills line — it is the term most job descriptions match on
- The tool the team already uses (Tableau, Power BI, Looker) named explicitly
- A decision or an amount attached to each analysis, not just the analysis
- Domain words from the industry you are applying to — churn, cohort, margin, funnel
Bullets you can adapt
Every one names a system or a surface, a change, and a number. Take the shape, not the figures — substitute your own and keep them defensible.
- Built the churn model that identified €1.8M of at-risk ARR; retention campaign recovered 34% of it
- Replaced a 40-tab spreadsheet with a dbt model and Looker dashboard used daily by 60 people
- Ran the pricing A/B test across 240k sessions that lifted checkout conversion 11%
- Cut month-end reporting from 5 days to 6 hours by automating the ETL in Python and Airflow
- Found and corrected a revenue-attribution error that had understated paid search by 18% for two quarters
- Segmented 1.2M customers into 6 cohorts; the top cohort drove 61% of repeat revenue
- Reduced dashboard load time from 40s to 3s by pre-aggregating in Snowflake
And the ones to cut
These are duties and adjectives. They describe a job title, not a person doing it.
- Analysed data to provide insights to stakeholders
- Created dashboards and reports using various BI tools
- Strong analytical and problem-solving skills
Keywords an ATS looks for
Include the ones that are true of you. Our free ATS checker scores your résumé against a specific job posting and shows the full keyword report.
Data Analyst résumé questions
Do I need Python on a data analyst résumé?
SQL is required; Python is a multiplier. If your Python is limited to pandas basics, list it and be ready to say exactly that — overstating it is the most common way analysts lose a technical screen.
How do I show impact when I only support decisions?
Name the decision and its size. "Analysis led the team to cut the mid-tier plan, which was 6% of revenue and 31% of support load" is impact even though you did not make the call.
Should I include dashboards I built?
Describe them by use, not by existence: who opens it, how often, and what they do differently because of it.