CV example
Data analyst CV example
A data analyst CV has one job: prove that your analysis changed something. This example shows an analyst who pairs every tool with the decision it informed and the result that followed.
Below you'll find a full example CV (also called a résumé), summaries for different experience levels, the skills and keywords employers search for, stronger bullet points, and the interview questions to prepare for.
Data analyst CV summary examples
Your summary is the first thing a recruiter reads. Two or three sentences: who you are, your strongest evidence, and what you want next. Here is the example CV's summary, plus versions for other levels.
Example CV
Data analyst with 3 years of experience in retail and fintech. I write SQL every day, build dashboards people actually use, and explain findings in plain language so teams can act on them.
Entry level
Economics graduate with strong Excel and SQL skills from coursework and a 6-month internship, where I built a churn report for a telecom team. Completed 3 portfolio projects in Python and Tableau.
Experienced
Data analyst with 6 years in e-commerce, owning the KPI framework and self-serve dashboards for a 200-person company. Partner with product and marketing to design experiments and size opportunities.
Skills to put on a data analyst CV
Hard skills
- SQL (joins, window functions, CTEs)
- Excel or Google Sheets (pivot tables, lookups, Power Query)
- A BI tool: Power BI, Tableau or Looker
- Python or R for analysis
- Statistics and A/B testing
- Data cleaning and validation
- Defining metrics and KPIs
Soft skills
- Explaining numbers to non-technical people
- Asking the right business question
- Curiosity
- Prioritising requests
Show soft skills through your bullet points rather than listing them on the CV itself.
Keywords recruiters and ATS look for
Many employers screen CVs with an applicant tracking system (ATS) that matches words from the job ad. These come up often in data analyst postings. Use the ones that are true for you, in the same wording as the job you're applying for. To compare your CV with a specific job ad, use the free CV keyword checker, or have Litjob tailor your CV to the job description for you.
- data analyst
- SQL
- Excel
- Power BI
- Tableau
- Python
- dashboards
- KPIs
- data visualisation
- reporting
- stakeholders
- A/B testing
For live figures, see the skills most requested in Data and AI job ads, updated weekly from thousands of postings.
Bullet points: before and after
Strong bullet points say what you did, how big it was and what changed. Compare:
WeakMade dashboards for the team.
StrongerBuilt a Tableau dashboard tracking 15 KPIs, now the weekly source of truth for leadership.
WeakAnalysed customer data.
StrongerSegmented 50,000 customers in SQL and found a high-value group that marketing now targets separately.
WeakDid reporting in Excel.
StrongerAutomated 6 weekly Excel reports with Power Query, saving 5 hours a week.
WeakHelped with experiments.
StrongerDesigned and analysed an A/B test on onboarding emails that raised activation by 6%.
Only use numbers you can explain in an interview. An honest estimate (“about 30%”) is better than a made-up exact figure.
Tips for writing a data analyst CV
Show the decision, not just the dashboard
For each project, say what the business did with your analysis. "Led to", "informed" and "resulted in" are your friends.
Add a portfolio if you are early in your career
Two or three projects on GitHub, Tableau Public or a simple site, with a short write-up, can stand in for experience.
Be specific about SQL and Excel
Everyone lists them. Mention what you do with them, like window functions, Power Query or building data models.
Mirror the tools in the posting
If a job asks for Power BI, put Power BI in your skills and in a bullet, as long as you have really used it.
Common data analyst interview questions
Your CV gets you the interview, and interviewers will ask about what's on it. Prepare for these:
- Walk me through an analysis that changed a decision.
- Use a real example: the question, your data, what you found and what the team did next.
- How would you find out why sales dropped last month?
- Show structure: check data quality first, then break it down by region, product, channel and time.
- Write a query to find the top 3 customers by revenue in each region.
- Expect a live SQL test. Practise window functions like ROW_NUMBER() and RANK().
- How do you handle a stakeholder who disagrees with your numbers?
- Explain how you would check the definitions together and walk through the data openly.