Data Analyst Resume Example (ATS-Friendly)

A data analyst resume that turns queries and dashboards into business results, with the tools, keywords and bullet formulas that recruiters and applicant tracking systems expect.

Last updated October 5, 2026

The example

Use this example

Lina Saeed

Data Analyst

lina.saeed@example.com · +966 55 000 0013 · Jeddah, Saudi Arabia

Summary

Data analyst with 4 years of experience in retail and e-commerce. Turns SQL, Python and Power BI work into decisions: a loyalty campaign that recovered USD 210,000 and dashboards used weekly by 35 managers.

Work Experience

Data Analyst, Souq Al Bahr E-commerce, 2022–Present

Built a Power BI sales dashboard used by 35 managers, replacing 6 manual reports
Found a 9% drop in repeat purchases with SQL cohort analysis, leading to a campaign that recovered USD 210,000
Designed an A/B test that raised checkout conversion by 4.2 points

Junior Data Analyst, Red Sea Retail, 2020–2022

Automated cleaning of 2 million monthly transactions with Python, cutting reporting from 3 days to 4 hours

Education

B.Sc. Statistics, King Abdulaziz University, 2020

Microsoft Certified: Power BI Data Analyst Associate (PL-300), 2023

Skills

SQL, Power BI, DAX, Python (pandas), Advanced Excel, Tableau, A/B testing, Data modelling, BigQuery

Opens in the free resume builder. Your draft stays on your device.

What employers look for

Hiring managers want analysts who answer business questions, not just people who know tools. Show the decision your analysis supported, such as a pricing change, a budget cut or a new campaign, and what it achieved.

Tools still matter for filtering. Most data analyst ads ask for SQL, Excel and one visualisation tool such as Power BI or Tableau, and many add Python or R. Name the ones you use regularly and how.

Communication is part of the job. Mention dashboards used by managers, reports you presented and how many people relied on your work, because analysis only creates value when others act on it.

Technical skills

SQL (joins, window functions)
Power BI and DAX
Tableau
Python (pandas)
Advanced Excel
Data cleaning and modelling
A/B testing and statistics
Data warehouses (BigQuery, Snowflake)

Soft skills

Business understanding
Data storytelling
Attention to detail
Curiosity

Professional summary examples by experience level

Pick the one closest to your level and replace the details with your own.

Entry level

Statistics graduate with 3 portfolio projects in SQL, Python and Power BI, including a sales dashboard built from public retail data. Detail-oriented and quick to turn questions into clear charts.

Mid level

Data analyst with 4 years of experience in retail and e-commerce. Builds Power BI dashboards used by 35 managers and uses SQL cohort analysis to find revenue opportunities worth over USD 200,000.

Senior

Analytics lead with 11 years of experience building data teams and self-service reporting for banks and retailers. Defines KPIs with executives and leads 6 analysts across SQL, Python and Tableau.

How to match your resume to the job ad and ATS

List the tools in the ad, such as SQL, Power BI, Tableau, Python or Excel, and put the ones you use in your skills section with the same names.
Match the business domain: sales, marketing, finance, operations or product. Lead with projects from the same domain.
Write bullets as question, method and result: what you analysed, how, and the decision or saving that followed.
If the ad mentions specific methods, such as forecasting, A/B testing or ETL, include them where they are true for you.

ATS keywords to include

Use the ones that truly match your experience, in the same words as the job ad.

SQL
Power BI
Tableau
Python
dashboard
KPI
data visualisation
ETL
data cleaning
stakeholder reporting

Achievement bullets you can adapt

Built a Power BI sales dashboard used weekly by 35 managers, replacing 6 manual Excel reports and saving 20 hours a month.
Identified a 9% drop in repeat purchases through cohort analysis in SQL, leading to a loyalty campaign that recovered USD 210,000 in revenue.
Automated data cleaning for 2 million monthly transactions with Python, cutting report preparation from 3 days to 4 hours.
Designed an A/B test for checkout changes that raised conversion by 4.2 percentage points.

Common mistakes to avoid

Listing tools without showing a business result from using them.
Writing “analysed data” without saying what data, how much and why.
Including dashboards with confidential company figures in a public portfolio.

Before you send your application

Many data roles welcome a portfolio. Put a link in your contact line, or export 2 or 3 short case studies with charts into one PDF and compress it before uploading.

If the portal limits file size, compress the PDF. If it accepts only one file, merge them into one PDF with your resume first.

Frequently asked questions

Do I need Python for a data analyst job?

Not always. SQL and Excel are the most requested, followed by Power BI or Tableau. Python is a strong advantage, especially for automation and larger datasets.

Should I include a portfolio?

Yes, if you can. A few public projects with clear questions, clean code and readable charts help, especially for career changers and graduates.

How do I show impact without exact figures?

Use percentages, time saved or the number of decision-makers who used your work when the figures are confidential.

Is this template ATS-friendly?

Yes. It uses one column, standard headings and real text, so applicant tracking systems can read every line.

Are certifications useful?

Certifications such as Microsoft PL-300 for Power BI or Google Data Analytics can help, mainly for junior roles. Experience and projects still matter more.

Should I list every Python library I know?

No. List the main ones you use, such as pandas and matplotlib, and show them through a project result.