How to write a data analyst CV

A data analyst CV should demonstrate your ability to turn data into insight. Employers are less interested in raw technical lists and more focused on how you apply analysis to inform decisions.

This guide explains how to write a data analyst CV that balances technical capability with real-world impact.

What Employers Look for in a Data Analyst CV

Recruiters typically assess:

• analytical thinking

• data interpretation skills

• tools and technologies used

• communication of findings

Your CV should show how analysis leads to action.

Structuring a Data Analyst CV

Professional Summary

Use this section to clarify:

• your analytical focus

• industries or domains worked in

• the type of problems you solve

For guidance on structuring this effectively, see our CV Writing Tips hub.

Technical Skills (With Context)

List tools such as:

• SQL, Python, R

• data visualisation platforms

• analytics frameworks

Where possible, connect tools to outcomes rather than listing them in isolation.

Analysis and Business Impact

This is the most important section. Highlight:

• insights delivered

• decisions influenced

• efficiency or performance improvements

This distinguishes analysts from data processors.

Tailoring a Data Analyst CV

Different roles emphasise different strengths — technical depth, stakeholder communication or domain expertise. Always adapt:

• language

• examples

• emphasis

If you’re transitioning into analytics from another role or thinking of a Career Change then our sister service Career Clarity may help.

FAQs

How long should a data analyst CV be?
Two pages is ideal for most professionals.

Should I include projects on a data analyst CV?
Yes — especially if they demonstrate problem-solving and impact.