Data analyst resumes are judged on whether the numbers you report translated into a decision someone actually made. "Analyzed data" is invisible — "analysis that changed a $400K budget decision" is not.
Contact → Technical Skills → Experience → Projects/Portfolio → Education. Putting Technical Skills near the top ensures SQL, Python, and BI tool keywords get matched immediately — analyst roles are among the most keyword-screened postings in tech hiring.
Every bullet should connect a technical action to a business outcome. "Built dashboards" is a task; "built a dashboard that changed a pricing decision" is a result.
List the exact tools and languages, grouped by category — recruiters and ATS both scan for specific technology names, not generic phrases like "data visualization."
A link to a GitHub repo, a public Tableau dashboard, or a Kaggle project gives hiring managers something concrete to click. For candidates with limited work experience, a strong portfolio project can carry as much weight as a job.
SQL/Python proficiency, a specific BI tool, evidence of business impact (not just technical output), and industry relevance if the role is domain-specific (finance, marketing, product). Those four things should be scannable immediately.
The most common mistakes are listing tools without evidence of using them meaningfully, describing analysis without stating the business decision it informed, omitting a portfolio link entirely, and using vague verbs like "analyzed" and "reviewed" instead of specific technical actions.
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