Data Analyst CV example
Data analyst CVs commonly read like tool inventories — SQL, Excel, Tableau, Python — but every applicant lists the same tools. What hiring managers actually screen for is whether your analysis changed a decision: a price adjusted, a campaign killed, a process redesigned.
Structure each experience bullet as analysis → decision → outcome. The tools become context inside the story rather than the story itself, and your CV immediately reads senior to anyone who has hired analysts before.
What recruiters look for in a data analyst CV
- SQL fluency stated plainly — it's the non-negotiable screening keyword
- Business impact: decisions, savings, or revenue your analysis influenced
- Dashboarding/BI work with named tools (Tableau, Power BI, Looker)
- Statistical literacy: A/B testing, regression, cohort analysis
- Stakeholder communication — analysts who can present are rarer than analysts who can query
Example professional summary
Data analyst with 5 years of experience turning messy operational data into decisions. Built the executive KPI suite used daily by 40+ leaders, and identified a pricing gap whose fix added $800K in annual margin. Advanced SQL and Python; strong A/B testing and stakeholder-presentation experience.
Example experience bullet points
Strong bullets lead with a quantified outcome and keep to one line. Adapt these to your own numbers — never copy claims that aren't true for you.
- Identified a regional pricing gap through cohort analysis; the corrected pricing added $800K annual margin
- Built 12 Tableau dashboards consolidating 5 data sources, cutting weekly reporting effort by 30 hours
- Designed and analyzed 25+ A/B tests for the growth team, raising trial-to-paid conversion 12%
- Automated the monthly finance reconciliation in Python, reducing close time from 5 days to 1
- Presented quarterly insight reviews to the executive team, directly shaping two product bets
Skills to include
Data Analyst CV writing tips
Translate analysis into money or time
"Saved 30 hours of weekly reporting" and "added $800K margin" are claims any executive understands. If a number is confidential, use percentages — "cut reporting effort 75%".
Separate the tool list from the achievement bullets
Keep the skills section as the keyword inventory for ATS matching, and spend your bullets on outcomes. Repeating "used SQL" in every bullet wastes your most valuable space.
Show the question, not just the query
"Investigated why churn spiked in Q3" demonstrates analytical thinking. Hiring managers want analysts who frame problems, not just execute requests.
Mention data volume when it's impressive
Working with billions of rows or streaming pipelines distinguishes you from spreadsheet-scale experience — but only claim it where it's true.
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