ATS Resume Checker for Data Analysts
SQL appears in 72.9% of 118 recent Data Analyst JDs; reporting in 67.8%. See which keywords your resume is missing.
Check your Data Analyst resume
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Based on 118 recent Data Analyst, Product Analyst, BI Analyst and Insights Analyst job descriptions from Greenhouse, Ashby, Lever, Arbeitnow, Working Nomads, Jobicy, RemoteOK.
See the keyword data firstWhat Data Analyst job descriptions ask for
Keyword coverage across 118 recent job descriptions. The percentages show how many JDs mention each term — not how many times it appears.
Top 8 by frequency
Full keyword map
All 32 terms that appeared in the sample, grouped by what they do. Percentages are the share of the 118 job descriptions that mention each term.
Query & programming
The baseline for most analyst roles. Show it in experience, not only in a skills list.
BI & visualization
Dashboards and reporting are the day-to-day output. Name the tool, then show the decision it changed.
Statistics & methods
Methods keywords separate analysts who can explain variance from those who only pull numbers.
Data stack & pipelines
Modern-stack terms are differentiators in this tech-heavy sample. Include only what you can defend.
Business & collaboration
The highest-frequency cluster. Pair each term with a stakeholder outcome, not a generic trait.
Source: Greenhouse, Ashby, Lever, Arbeitnow, Working Nomads, Jobicy, RemoteOK; collected Oct 19, 2026.
See matched vs missing before you rewrite
After you upload your resume and paste a Data Analyst job description, the checker separates what your resume already proves from what it is missing. This example uses the keywords above.
Check my resume freeExample match
Data Analyst · example JD
Matched
Missing
Illustrative output. Your results depend on your resume and the job description.
What this means for your Data Analyst resume
SQL is table stakes
72.9% of the sampled Data Analyst job descriptions mention SQL. Put SQL in your experience bullets, not only in a skills list.
Stakeholder work beats tools
66.1% mention stakeholder or cross-functional work — more than Tableau (41.5%) or Excel (16.1%). Show who you worked with and what decision your analysis changed.
Excel alone is not enough
Only 16.1% mention Excel; Power BI appears in 26.3%. Python (46.6%) and Tableau (41.5%) appear more often. Excel is still useful, but it should not be your only data tool.
How to use these keywords without stuffing
A keyword map is a menu, not a checklist to paste into every section. Use the term where it is true, and prove it in the same bullet.
Mirror the JD's exact wording
If the posting says “stakeholder management”, use that phrase instead of a synonym. Include both the acronym and the full form when relevant (A/B testing, experimentation).
Put the keyword inside a result
The skills section gets parsed; the experience bullet gets read. Pair the tool with scale and outcome — users, revenue, latency, hours saved, decision changed.
Cover the cluster, not one word
A JD asking for SQL usually also mentions adjacent tools or methods. Show the cluster in one bullet instead of repeating the same keyword everywhere.
A safe Data Analyst skills-section order
Querying & languages → BI & reporting → statistics & methods → data stack. If the JD names R, pandas/NumPy, data cleaning, a data warehouse or a certification, add it only when you can prove it. This sample is SQL/Python-heavy, so absence from the map is not evidence that a term is irrelevant.
Common Data Analyst resume mistakes
These are the patterns that make a keyword-rich resume read like a tool inventory instead of evidence.
Tool soup
Listing every tool you have touched without saying what you built or changed. For a Data Analyst, a tools-only skills section is weak because SQL and reporting need proof in experience bullets.
Keyword stuffing
Repeating the same term in the summary, skills list and every bullet. Recruiters notice; it reads like SEO copy, not evidence.
Ignoring the human reader
ATS keywords get you parsed; stakeholder communication and outcomes get you interviewed. Use the keyword as the noun inside a result, not as the whole sentence.
Check your Data Analyst resume against a real JD
Upload a PDF or DOCX, paste the job description, and get the matched and missing keywords.
Before / after: turn duties into evidence
ATS keywords are necessary, but hiring managers still read the bullet. Put the keyword inside a result. These are illustrative rewrites based on the keyword data above.
Responsible for reports and dashboards.
Built 12 Tableau dashboards on SQL/Snowflake for 5 stakeholder teams, cutting weekly reporting from 6 hours to 45 minutes.
Used Python for data analysis.
Automated a Python/pandas pipeline that cleaned 2M+ rows and reduced manual QA time by 80%.
Worked with business teams.
Partnered with product and finance stakeholders to define 8 KPIs and improve forecast accuracy by 15%.
How we built this page
We collected 118 recent postings titled Data Analyst, Product Analyst, BI Analyst and Insights Analyst from Greenhouse, Ashby, Lever, Arbeitnow, Working Nomads, Jobicy, RemoteOK on Oct 19, 2026. Duplicate postings were removed. A keyword counts once per JD; percentages are the share of JDs that mention it.
The sample skews toward tech and remote-first companies. Finance, healthcare and traditional-industry postings are present but still underrepresented, so treat the percentages as directional rather than universal.
Method and limitations
- Source mix: Greenhouse 49 · Ashby 33 · Lever 9 · Arbeitnow 3 · Working Nomads 1 · Jobicy 4 · RemoteOK 19.
- The before/after bullets are illustrative, not real candidate resumes.
- This is an ATS-style keyword and formatting check, not a reproduction of any employer's private screening system.
Data Analyst resume FAQ
What keywords should a Data Analyst resume have?
In our 118-JD sample, the highest-frequency terms were SQL (72.9%), reporting (67.8%), stakeholder management (66.1%), dashboards (60.2%), requirements (50.0%) and Python (46.6%). The full map above groups 32 terms by querying, BI, statistics, data stack and business skills. Use the exact terms from each job description, not a generic list.
How many keywords should a Data Analyst resume have?
Aim for roughly 15–25 role-specific terms across your skills and experience sections, not a separate keyword dump. Start with the terms that appear in the job description, then keep only the ones you can prove with a project or result. The full map above is a menu, not a checklist to paste in.
Should I copy the exact wording from the job description?
Yes for tools, methods and job titles, as long as it is truthful. If the posting says 'stakeholder management', use that phrase instead of a synonym; include both the acronym and the full form when relevant (A/B testing, experimentation). Exact phrasing helps matching, but stuffing the same term into every bullet does not.
Is Excel enough for a Data Analyst resume?
Not in this sample. Only 16.1% of the 118 job descriptions mention Excel, while 26.3% mention Power BI. SQL, Python, Tableau and dashboard/reporting work appear more often. Excel is still useful, but it should not be your only data tool.
How do I check my resume against a specific Data Analyst job?
Paste the job description into the free Jobalytics checker, upload your resume, and scan. You will see the keywords the job asks for, which ones are missing, and where to add them.
Is this a real ATS score?
No. This is an ATS-style keyword and formatting check, not a reproduction of any employer's private screening system. Different ATS platforms and companies rank candidates differently. Use it to find gaps, not as a guarantee.
How many job descriptions is this page based on?
118 recent Data Analyst, Product Analyst, BI Analyst and Insights Analyst job descriptions from Greenhouse, Ashby, Lever, Arbeitnow, Working Nomads, Jobicy and RemoteOK, collected Oct 19, 2026. We show the sample size and collection date on the page.
Is the check free?
The first resume scan is free and does not require signup. Paid plans are only for AI rewriting, unlimited scans and exports.
Related role checkers
The same method with role-specific job-description data.
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