Resume keywords for a Data Analyst
Analyst postings screen for three things in combination: a query language, a visualisation tool, and evidence that the analysis changed a decision. Candidates reliably cover the first two and omit the third, which is the one that separates a shortlist from a rejection.
The keywords, grouped
Query and manipulation
Visualisation
Analysis
Data handling
Business
Check your resume against a real posting
A checklist tells you what the market asks for. Paste an actual job description and your resume to see which of these terms you are already covering and which you are not — free, and the resume never leaves your browser.
Run the checkHow to evidence these on the page
- Close the loop on every analysis: what you measured, what you found, and what changed as a result. "Identified a 22% drop-off at checkout step three; the fix recovered ₹4L monthly" beats any dashboard count.
- Name the data volume and the refresh cadence. "Daily dashboard over 12M rows" is concrete; "large datasets" is not.
- Say SQL explicitly even if it is obvious from the tooling. It is the single most-screened term in this role.
- If you automated a manual report, state the hours saved per week — it is the easiest quantification in the job and almost nobody includes it.
What gets these resumes rejected
- Listing tools without a single business outcome, which reads as someone who produces charts nobody acts on.
- Claiming "machine learning" for what was a spreadsheet trendline. Interviewers probe this immediately.
A caution about keyword stuffing
Every term above is worth including only if it is true of you. Padding a resume with unearned keywords gets you into interviews you then fail, which is worse than not being shortlisted. Use this as a prompt to surface experience you already have and forgot to write down — not as a list to copy.