How does your resume hold up for a Data Analyst role?
Paste a Data Analyst posting and your resume. Get the score, the gaps, and what this role's ATS screens actually check for.
"Advanced Excel" with no SQL is the single most common gap on analyst resumes — most postings now treat SQL as the baseline, not a bonus skill worth listing separately. The bigger tell, though, is a resume full of reports and dashboards with no sign of what any of them changed — a report that didn't move a decision is hard to distinguish from one that was never read. Check yours against a real posting and see exactly which requirements it's asking for that your resume doesn't currently back up. This isn't unique to any one industry either — the same gap between reports produced and decisions changed shows up whether the postings you're targeting are in fintech, retail or SaaS.
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What this role's postings ask for
- SQL
- Excel / Google Sheets
- Python (pandas)
- Tableau / Power BI / Looker
- A/B testing
- Statistics
- Dashboarding
- ETL
- Data cleaning
- Stakeholder reporting
- KPI definition
- Cohort analysis
- Forecasting
See how often each of these actually appears in live postings →
Common mistakes on resumes for this role
- "Advanced Excel" listed with no SQL — most analyst roles now expect SQL as the baseline skill, not a bonus, and its absence from a resume reads as a gap even when the candidate actually knows it.
- Bullets list reports produced, not decisions they changed — "built a weekly dashboard" says nothing about what it was used for or whether anyone acted on it.
- No mention of data cleaning or quality work, which is most of the actual job and a strong signal of rigor, but rarely makes it onto a resume because it feels unglamorous to list.
- Statistics vocabulary missing (significance, confidence interval, cohort) even when the underlying work clearly involved it, which makes analytically rigorous work read as purely descriptive.
- Tool names listed with no scale or cadence attached — "used Tableau" doesn't say whether it was a one-off chart or a dashboard refreshed daily for a leadership team.
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Weak vs. strong
Weak Created dashboards to track sales performance.
Strong Built a Looker dashboard that surfaced a 15% regional churn spike, prompting a retention campaign that recovered $180K in ARR.
What "evidence" means for this role
For an analyst resume, evidence is the decision or dollar figure downstream of the analysis, not the analysis itself. "Created dashboards to track sales performance" evidences that a dashboard exists; it says nothing about whether it mattered. "Surfaced a 15% regional churn spike, prompting a retention campaign that recovered $180K in ARR" evidences the finding, the action it caused, and the result — the three things a hiring manager is actually trying to judge. When SteadyCV checks your resume, this is the pattern it's looking for: requirements the posting names (a tool, a method, a type of analysis) that your resume backs up with an outcome, not just a task. The same test applies to forecasting work: a forecast that was never checked against what actually happened is a weaker claim than one where you can say how close it landed.
A closer look at the requirements that matter most
- SQL
- SQL is now assumed rather than requested outright in a growing share of postings, which means its absence from a resume is read as a gap rather than neutral. If you write SQL regularly, say so plainly and, if you can, name the complexity — joins across multiple tables, window functions, query optimization — rather than letting it hide inside a generic "data tools" line.
- A/B testing
- Postings that ask for experimentation experience are usually screening for whether you understand a control group and statistical significance, not just whether you've seen a test run. A bullet naming a test you designed or read, and what the result changed, evidences judgment that "data-driven" as a phrase alone doesn't.
- Stakeholder reporting
- This requirement is really asking whether you can translate a finding for someone who doesn't read SQL — a skill that's invisible in the analysis itself and only shows up if you describe the audience and what they did with what you told them.
- Python (pandas)
- Postings that name pandas specifically, rather than just "Python," are usually distinguishing analysts who script repetitive Excel work away from analysts who only ever click through a UI. If you use pandas for data cleaning or larger-than-Excel datasets, naming that scale — row counts, join complexity, how often the script runs — is what separates it from a resume line that just lists the library.
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