Data Analyst Resume: Example, Skills and Stack to Write
In short
For an analyst, recruiters and the ATS look for tools and impact on decisions: SQL, Python, BI — and above all, which decisions your data drove. 'Built reports' with no effect is a process. Stack keywords must match the posting.
What recruiters and the ATS look for in a data analyst resume
- Stack: SQL, Python/R, BI (Tableau, Power BI, Metabase), Excel
- Impact: which decisions/dollars sit behind your analysis
- Type of analytics: product, marketing, finance, BI
- A/B tests, metrics, dashboards, data quality
ATS keywords
These phrasings come from real job postings — include the ones your experience backs up so the system can match you:
Strong bullet examples: before → after
Replace duties with results. Numbers (N — use your real ones, never invent them) turn a line into proof:
Common mistakes in a data analyst resume
- Stack with no result: 'know SQL' instead of 'used SQL to find N, which drove N'.
- No link from analysis to a decision or money — value invisible.
- Keywords not matched to the posting (product vs BI vs finance analyst) — weak ATS match.
Review your resume for free
Upload your resume and the job posting — get an honest 0–100 score on hiring criteria and a list of concrete fixes for your role. No sign-up. And if you want, Offerly rewrites your resume for the job, without inventing your numbers.
Review my resume →Frequently asked questions
Which skills should a data analyst resume list?
SQL almost always, plus Python/BI for the posting, and crucially — examples of decisions driven by data. Tools without results don't convince.
How do I show impact when I didn't make the decision?
Describe the insight you delivered and what followed: hypothesis, test, metric, effect. That's an analyst's value.
Do I need different resumes for product vs BI analyst?
Yes. The roles want different stacks and experience. Tailor the resume to the specific posting — it improves ATS pass-through and the first screen.