Resume Example
Data Analyst Resume Example
Emphasize analytical rigor and business outcomes. This sample spotlights dashboards, insights, and decision support.
Modern Minimal
Clean sidebar layout with navy accent. Great for tech and finance roles.
Keywords
Sample bullets
- Built KPI dashboards that reduced weekly reporting time by 6 hours.
- Identified churn drivers that led to a 14% retention lift.
- Automated SQL reporting for 5 departments with 99% data accuracy.
Certifications
- Google Data Analytics Professional Certificate
- Microsoft Power BI Data Analyst (PL-300)
- CompTIA Data+
Why this works
- Highlights decision-making impact tied to revenue or cost.
- Demonstrates strong SQL and dashboarding expertise.
- Shows stakeholder communication and narrative clarity.
Step-by-Step Guide
How to Write a Data Analyst Resume
Lead with business impact
Data analysts who get hired show how their analysis drove decisions. Your summary should connect your technical skills (SQL, visualization) to business outcomes (revenue insights, cost savings, operational improvements). Position yourself as someone who turns data into action, not just reports.
Structure experience around the insight-to-action loop
For each role, describe what questions you answered, how you found the answer, and what changed as a result. 'Built dashboard' is incomplete; 'Built executive dashboard that identified $500K in at-risk revenue, leading to retention campaign that saved 80% of flagged accounts' shows the full value chain.
Highlight your technical toolkit
Include a clear skills section with: SQL (specify dialects if relevant), visualization tools (Tableau, Looker, Power BI), statistical tools (Python, R), and spreadsheet expertise. Mention data cleaning, ETL experience, and any automation you've built.
Show stakeholder communication skills
Data analysis is only valuable if stakeholders act on it. Highlight how you presented findings to non-technical audiences, translated complex analysis into recommendations, or trained teams to self-serve with dashboards you built.
Include relevant projects or portfolio
Link to Tableau Public dashboards, GitHub repositories with analysis notebooks, or a portfolio site. Showing actual work is more compelling than describing it. If you can't share work, describe projects in enough detail that readers understand your analytical approach.
Summary Examples
Good vs. Bad Resume Summaries
“Data analyst with 4 years translating complex datasets into actionable business insights. Built automated reporting infrastructure that saved 20+ hours/week and identified $2M in revenue opportunities through customer segmentation analysis.”
Clear value proposition (insights to action), efficiency gains quantified, and a significant business impact number.
“Detail-oriented analyst with strong SQL skills and experience creating reports. Proficient in Excel and data visualization.”
Generic skills list without context. 'Detail-oriented' is filler. No indication of business impact or analytical sophistication.
“Marketing analytics specialist helping growth teams optimize $5M annual ad spend. Reduced CAC by 35% through channel attribution modeling and built experimentation framework that increased test velocity 3x.”
Domain expertise (marketing), impressive scope ($5M spend), specific achievements with metrics, and strategic contribution.
“Analytical professional looking for opportunities to leverage my data skills in a dynamic organization. Quick learner with a passion for numbers.”
Pure filler with no substance. 'Passion for numbers' doesn't tell hiring managers anything useful about your capabilities.
Action Verbs
Power Words for Data Analyst Resumes
Common Mistakes
What to Avoid
- ✗Listing tools without showing analytical thinking or business impact
- ✗Focusing on data pulled rather than insights generated and actions taken
- ✗Missing the 'so what'—every analysis should connect to a decision or outcome
- ✗Underselling stakeholder communication and presentation skills
- ✗Not including a portfolio link when you have shareable work
- ✗Using generic phrases like 'analyzed data' without specificity about what, why, and what happened next
Salary ranges
| Level | US | EU | Canada |
|---|---|---|---|
| Entry | USD 60,000-80,000 | EUR 43,000-56,000 | CAD 65,000-85,000 |
| Mid | USD 80,000-111,000 | EUR 56,000-70,000 | CAD 85,000-110,000 |
| Senior | USD 111,000-140,000 | EUR 70,000-90,000 | CAD 110,000-130,000 |
Market themes
- 70% of analysts use AI tools
- Hybrid work is standard
- Cloud skills are increasingly required
US hot markets
- New York
- San Francisco
- Seattle
- Boston
- Chicago
EU hot markets
- London
- Amsterdam
- Berlin
- Munich
Canada hot markets
- Toronto
- Vancouver
- Calgary
FAQ
Common questions about this role
What should a data analyst resume include?
SQL proficiency, analytics tooling (Tableau, Looker, Power BI), visualization skills, and examples of insights that influenced business decisions. Show the full arc from data to insight to action.
How do data analysts show impact on their resume?
Link dashboards or analyses to revenue, retention, or efficiency improvements. Quantify results: 'Built churn prediction model that identified $2M in at-risk ARR' demonstrates clear business value.
Which technical skills are most important for data analysts?
SQL is essential. Add Python or R for statistical analysis, Excel for quick modeling, and at least one BI tool (Tableau, Looker, Power BI). Mention A/B testing, statistical methods, and data cleaning skills.
Should data analysts include a portfolio or project links?
Yes, if possible. Link to Tableau Public dashboards, GitHub repositories with analysis notebooks, or a personal site with case studies. Showing actual work is more compelling than describing it.
How do I transition from another role to data analyst?
Highlight analytical work from your current role—reporting, Excel modeling, or process improvements driven by data. Complete SQL and visualization certifications, and build portfolio projects using public datasets.
What metrics should data analysts put on their resume?
Time saved through automation, accuracy improvements, revenue impact from insights, and stakeholder adoption of dashboards. 'Reduced weekly reporting time by 8 hours' shows tangible efficiency gains.
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