Data Analyst Resume Template & Examples

1. 1Contact & LinkedIn
2. 2Professional summary
3. 3Technical skills (SQL, BI tools, Excel)
4. 4Work experience (impact bullets)
5. 5Projects / dashboards (with links)
6. 6Education & certifications

• SQL querying
• Data visualization (Power BI / Tableau)
• Advanced Excel / spreadsheets
• Data cleaning & transformation
• Descriptive statistics
• Dashboard design
• Business/KPI reporting
• Communicating insights

Applicant tracking systems scan for role-specific terms. Include the ones below that are genuinely true of you — ideally mirroring the exact wording in each job description.

• SQL
• Excel
• Power BI
• Tableau
• Python
• data visualization
• dashboards
• ETL
• data cleaning
• statistics
• A/B testing
• KPIs
• reporting
• Google Analytics
• stakeholder reporting

Strong bullets lead with an action verb and end with a measurable result. Use these as patterns — swap in your own real numbers and context.

• Built a Power BI sales dashboard consolidating 6 data sources, cutting weekly reporting time from 8 hours to 20 minutes.
• Wrote SQL queries to analyse 2M+ transactions, uncovering a pricing gap that recovered ₹40L in annual margin.
• Segmented customers by RFM analysis, enabling a targeted campaign that lifted repeat-purchase rate 15%.
• Automated a monthly KPI report with Python, eliminating a recurring 2-day manual process.
• Cleaned and reconciled a customer dataset with 12% duplicate records, improving CRM data accuracy for the sales team.
• Identified a 22% drop-off in the signup funnel via Google Analytics, informing a fix that improved conversion 11%.

• Saying "analysed data" without naming the tool, the dataset size, or the resulting decision.
• Listing Excel and SQL as skills but showing no evidence of using them for real outcomes.
• Omitting links to dashboards or a portfolio that prove your visualization work.
• Focusing on tasks ("created reports") instead of the impact those reports drove.
• Overstating tool expertise — be honest about the difference between basic and advanced proficiency.

For a data analyst, we suggest the ATS Safe template. The ATS Safe template offers a tidy skills-and-experience structure that reads well for analytics roles and maximises ATS compatibility.

Data Analyst resume FAQs

What is the difference between a data analyst and data scientist resume?

A data analyst resume emphasises SQL, BI tools (Power BI, Tableau), Excel, dashboards, and business reporting. A data scientist resume leans further into machine learning, statistics, and programming. Tailor keywords and projects to the exact job description rather than mixing both signals.

Do I need Python on a data analyst resume?

It's a strong plus but not always required. Many analyst roles run on SQL, Excel, and a BI tool. If the job description mentions Python or R, and you can use it, include it. If it doesn't and you can't, don't force it — depth in SQL and visualization often matters more.

How do I show impact as an analyst?

Tie each analysis to a decision, a time saved, or money made/protected. "Built a dashboard that cut reporting time from 8 hours to 20 minutes" or "analysis recovered ₹40L in margin" are far stronger than "created dashboards and reports."

Should I include a portfolio link?

Yes. A link to a portfolio, a public Tableau/Power BI profile, or a GitHub with notebooks lets reviewers verify your work. Make sure any shared dashboards use dummy or anonymised data — never expose real employer data.