What Recruiters Scan First on a Data Analyst Resume
The first pass is short. Recruiters check if your resume parses cleanly, whether bullets show decisions backed by data, and if your toolset matches the role. Our Honest ATS Scanner mirrors that pass: it flags parser risks, vague verbs, weak headings, and keyword gaps across SQL/Excel/Python and BI tools.
Role-True Keywords (Use Them Naturally)
Include the skills the job expects—then tie them to business outcomes:
- SQL (joins, window functions, CTEs, query tuning), DB basics
- Excel (pivot tables, Power Query, LOOKUP/XMATCH, dynamic arrays)
- Python (pandas, NumPy, matplotlib), notebooks, basic stats
- BI: Tableau, Power BI (DAX), Looker; dashboard lifecycle & governance
- ETL/ELT, data quality, lineage, stakeholder reporting
- Experimentation: t-test, regression, A/B test readouts
Keywords help ATS; outcomes win interviews. See how to write keyword bullets and How WisGrowth Works.
Common Analytics Resume Mistakes (We’ll Flag Them)
- Lists without results: “SQL • Excel • Tableau” with no impact.
- Unparseable layouts: multi-column image templates that break ATS.
- Ambiguous bullets: “analyzed data” without a decision or metric change.
- Dashboard-only stories: no stakeholder use case or adoption stats.
- No scope: dataset sizes, latency, users, or business unit not mentioned.
Outcome-Driven Bullet Examples (Copy the Shape)
- Rebuilt lead-routing SQL; SLA breaches −35%, conversion +1.8 pts.
- Automated weekly Excel → Power Query pipeline; 4 hrs/week saved for FP&A.
- Designed churn dashboard (Power BI + DAX); flagged risk cohorts → churn −2.2 pts.
- AB test readout (p<.05): simplified pricing table → checkout +2.6 pts.
Lead with the change, then show how data enabled it (query, model, dashboard, test). If numbers are confidential, use relative deltas or operational proxies.
Parsing & Structure: Don’t Lose to the Template
Use one column, real text, standard headings (Experience, Education, Skills), and readable fonts. Our scan grades parseability, structure, and keywords, then gives specific edits so you can fix the top blockers quickly.
7-Day Plan to Lift Your Analytics Signal
- Run a free scan to baseline ATS and find your top three issues.
- Rewrite three bullets with business problem → method → metric change.
- Add dataset scope: rows, tables, refresh cadence, users, or latency target.
- Ship a mini case: “From ad-hoc to automated”—before/after time saved & error rate.
- Refresh LinkedIn headline: “Data Analyst • SQL/Python • time saved −28%, churn −2.2 pts”.
- Apply to three aligned roles and message two stakeholders weekly.
New to Analytics or Switching From Another Role?
Translate prior work into data outcomes: reconciled reports (accuracy), faster decisions (time-to-insight), demand forecasting (MAPE), or cost controls. Build 1–2 public projects (portfolio starter) and align your resume with an ATS scan. Not sure which lane (reporting vs. product analytics vs. finance analytics)? Take the Clarity Quiz.
Junior vs. Senior Analyst: What Changes
Junior: show learning velocity, clean SQL, and stakeholder empathy—automations and QA help. Senior: emphasize scope (multi-team models), data quality ownership, experimentation design, and decisions shipped with PM/Eng/Finance partners.
Keep Going
When your resume tells a story of decisions enabled by data, interviews get easier. Improve one bullet, one project, and one calibrated application each week. For fast direction, try the 60-sec Career Health Check, then read How WisGrowth Works.