Data analyst postings filter on the specific tool stack, not the general idea of "data-driven" work: SQL and Python are named separately from a BI platform (Tableau vs. Power BI aren't interchangeable to an ATS), and "analyzed data" credits nothing compared to naming the actual technique — A/B testing, regression, cohort analysis. A CV that says "provided insights to stakeholders" without naming the query language, the dashboard tool, and the statistical method loses to a weaker candidate who named all three. Yoxon's CV builder automates exactly that — it rewrites your bullets around the specific keywords each job posting uses.
These are exact-match terms — not synonyms — that recruiters commonly build screening filters around for this role. If you genuinely have the experience, make sure the specific term appears in your CV, not just a paraphrase of it. Before you spend that time, it's worth confirming the listing is a real, active opening — run it through our free ghost job detector first.
And before you rewrite your resume, it's worth checking how your CV or LinkedIn profile itself reads — run our free Resume Auditor to see your score and get rewrite suggestions.
Example 1
Analyzed data to help the team make better decisions.
Built 8 Tableau dashboards tracking core product KPIs from a 40M-row SQL warehouse, cutting the weekly reporting cycle from 3 days to 4 hours across 3 stakeholder teams.
Example 2
Ran experiments to test new features.
Designed and analyzed 15+ A/B tests in Python (SciPy/statsmodels) on checkout-flow changes, identifying a variant that lifted conversion 6.2% at 95% statistical significance.
Should I list SQL and Python separately, or just say 'data analysis tools'?
List each language by name — an ATS filter set up for "SQL" or "Python" won't credit a generic phrase like "data analysis tools" as a match, and a recruiter skimming for a specific stack is looking for the exact keyword, not a category it belongs to.
Does it matter whether I say Tableau or Power BI if I've used both?
Yes — list both if both are genuine, but lead with whichever the posting names. The two tools are scored as different keywords by most ATS configurations, not as synonyms for "BI tool experience," so naming only one when you know both under-represents you against a filter set up for the other.
How do I quantify analysis impact when the 'metric' is really a decision, not a number?
Reconstruct the scale around the decision instead: the size of the dataset, the number of stakeholders or teams who used the dashboard, the dollar value of the budget it informed, or the frequency it's referenced (weekly report, quarterly review) — a specific scale number is more convincing than "informed key business decisions" with nothing attached.
Applying in a different field? Our Mechanical Engineer CV guide covers the same keyword-matching approach for that role.
More role-specific CV guides