Data Analyst
Sample Resume & ATS Keywords
Data Analyst is one of the highest-volume titles in tech, and it now has a real dedicated resume competitor in the market — generic advice won't be enough to stand out. Recruiters filter hard on tool-specific keywords (SQL, Power BI, Tableau) rather than job-title history alone, so the gap between a strong analyst and a findable one is almost always keyword formatting. The example below shows what that looks like.
All sample resume content on this page is original and illustrative — fictional candidates, realistic numbers. Use it as a pattern, not a template to copy verbatim.
Sample Data Analyst resume summary
What a parseable, keyword-complete professional summary looks like for this role:
Data Analyst with 4 years supporting marketing and operations decisions for a mid-size DTC retailer. Owns SQL-based reporting and 15+ Tableau dashboards used by 60 stakeholders across marketing, ops, and finance. Runs cohort analysis and A/B test readouts for the growth team and holds the Google Data Analytics Certificate. Comfortable presenting findings directly to non-technical leadership.
Sample achievement bullets that pass ATS screening
Each bullet follows the pattern recruiters and parsers reward: exact keywords, a specific action, and a quantified outcome.
- Built and maintained 15+ Tableau dashboards tracking marketing and operations KPIs, used weekly by 60 stakeholders across 3 departments.
- Wrote SQL queries against a 40-table warehouse to support ad hoc analysis requests, cutting average turnaround time from 3 days to 6 hours after building a self-serve query library.
- Ran cohort analysis on customer retention data, identifying a segment with 2.3x higher LTV that became the target audience for a $400K acquisition campaign.
- Delivered A/B test readouts for 20+ growth experiments per year, with statistically validated results directly informing 12 shipped product and marketing changes.
- Automated a manual weekly reporting process (SQL, Power Query) that previously took 6 hours, cutting it to 30 minutes and eliminating recurring data-entry errors.
- Partnered with finance to build a unified revenue-and-margin dashboard, replacing 4 conflicting spreadsheets and becoming the department's single source of truth.
- Presented quarterly business reviews to non-technical leadership, translating dashboard findings into 3 recommended actions adopted each quarter on average.
- Audited data quality across the customer database, identifying and flagging a duplicate-record issue affecting 8% of records before it skewed a board-level metric.
ATS keyword bank for Data Analyst resumes
From our 2026 research into recruiter sourcing behavior for this role. Recruiter and ATS searches match exact strings — carry the terms your real experience supports, in the wording the posting uses.
| Keyword group | Terms recruiters search |
|---|---|
| Titles | Data Analyst · Business Analyst · Reporting Analyst · BI Analyst |
| SQL & BI tools | SQL · Power BI · Tableau · Looker |
| Analysis vocabulary | dashboarding · KPI tracking · A/B testing · cohort analysis |
| Certifications | Google Data Analytics Certificate · Microsoft Power BI Data Analyst Associate |
| Outcomes | dashboards built · reporting time reduced · stakeholders served · data accuracy |
Data Analyst resume formatting: do this, not that
Do
- Name SQL and BI tooling by exact product: SQL, Power BI, Tableau, Looker.
- Use reporting and analysis vocabulary directly: dashboarding, KPI tracking, A/B testing, cohort analysis.
- Quantify dataset and stakeholder scale: records processed, dashboards built, teams served.
- List certifications as systems index them: Google Data Analytics Certificate, Microsoft Power BI Data Analyst Associate.
- Show business impact, not just outputs — link a dashboard or query to a decision that changed because of it.
Don't
- Don't write "analyzed data and created reports" — name the tool, the dataset, and the decision it informed.
- Don't blur Data Analyst with Data Engineer or Data Scientist vocabulary — they're separate, distinctly searched roles.
- Don't list SQL as a bare skill without showing scale — table count or query complexity signals real depth.
- Don't omit stakeholder count or department — it calibrates the size and visibility of your work.
- Don't present dashboards as the end product — tie each one to a decision or KPI it moved.
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