Skills employers ask for most
The most-requested skills in data analyst job postings, per published hiring research. Put the ones you have in a dedicated Skills section and prove the top few in your bullets.
- SQL53%
- Microsoft Excel41%
- Python31%
- Power BI29%
- Problem solving29%
- Tableau26%
- R25%
- Machine learning14%
- Presentation skills14%
Source: 365 Data Science, Data Analyst Job Outlook (2026) — share of analyzed US data-analyst job postings requesting each skill.
Example data analyst résumé
Resunova score 89/100Data analyst with four years of experience turning ambiguous business questions into clear, quantified recommendations using SQL, dashboards, and statistical analysis. Builds self-serve reporting that replaces manual pulls, and partners directly with finance, merchandising, and operations leaders to size opportunities and measure impact after launch. Surfaced a promotional pricing inefficiency worth $1.2M in annual margin through margin decomposition. Now owns executive reporting for 12 business units and the shared metric dictionary behind it.
A complete, scored data analyst / analytics specialist résumé (fictional candidate) built with Resunova. Notice every bullet leads with a strong verb and ends in a measurable outcome — that pattern is what earns the score above. Score your own résumé free →
Salary & where these roles work
From 208 analyzed data analyst postings. Figures vary by location and seniority.
Data Analyst resume FAQ
What skills should a data analyst resume highlight?
SQL is the single most-requested skill, appearing in 53% of analyst postings (365 Data Science, 2026). Pair it with Excel (41%), Python (31%), and a visualization tool (Power BI 29%, Tableau 26%), and quantify the decisions your analysis drove.
Should a data analyst resume include a portfolio?
Yes. A link to dashboards, a GitHub with SQL or Python notebooks, or a written end-to-end analysis sets you apart from keyword-only résumés.
How do I quantify data analyst impact on a resume?
Tie each analysis to a business outcome (revenue found, hours saved, churn reduced, decisions accelerated) rather than writing 'analyzed data using SQL.'
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