The Data Analyst resume
Analysts get hired for decisions they changed, not dashboards they built. Every project should end with what someone did differently.
What screeners actually look for
Three things, in order of weight.
01
The decision your analysis changed and what happened next
02
Whether you designed the metric system or only queried it
03
Depth of method — is it counting, or is it causal
The three mistakes this role makes most
- 'Built dashboards and produced daily reports' with no outcome
- Listing SQL and Excel as the entire skill set
- No business context — the reader can't tell if the number was big
Keywords worth including
Work them into your title, project descriptions and skills — don't pile them into a list.
Templates that fit Data Analyst
Each cover is the template itself, rendered.
Room for long analytical bullets.
Light accent keeps a data resume from reading flat.
Chinese-format header if you're applying domestically.
Common questions
How long should a Data Analyst resume be?
One page. Unless you have 10+ years and a genuinely full second page, the second page only dilutes the first. Put the decision your analysis changed and what happened next above the fold.
What gets a Data Analyst resume rejected fastest?
'Built dashboards and produced daily reports' with no outcome. It is the single most common reason this role gets screened out, and fixing it matters far more than changing templates.
Can I generate a Data Analyst resume with AI?
Use AI for the first draft, then verify every claim and number yourself and reorder keywords against the target job description. AI handles speed; the content is still your responsibility.