Dig into our data analyst CV example
Build a professional CV that gets you hired.
Creating a CV recruiters will love to analyse
Ensure keywords are included
You might not realise it, but CVs include their fair share of data analysis. CV scanning software filters CVs before recruiters even read them. To pass this hurdle, add plenty of keywords to your CV based on the job description.
Back it up with data
As a data analyst, you’ll know better than anyone how important numbers are. Make sure you include plenty of figures relating to your jobs, such as the number of KPIs you worked with, or an increase in sales you contributed to.
Organise your CV
While you might be an expert at interpreting vast amounts of raw data, recruiters are not. Organise your CV into clear, concise sections such as experience, essential skills, and qualifications, so recruiters can skim read and pick out the information they need.
Frequently asked questions about data analyst CVs
What qualifications do I need to be a data analyst?
You don’t need formal qualifications to be a data analyst. However, many employers prefer candidates with degrees in subjects like:
- Mathematics.
- Computer Science.
- Business Information Systems.
- Statistics.
Can I be a data analyst without a degree?
You can be a data analyst without a degree, though it may take you longer to progress. When you build your CV, focus on demonstrating skill in and understanding of the following:
- Data conversion and migration.
- How to carry out an audit.
- The use of technologies like SQL, JavaScript, SAP PowerBuilder, and RapidMiner.
Whatever your experience, we can help you to build a perfect CV with our CV builder. Our templates, pre-written text and advice mean it’s quicker and easier than going it alone. Start now!
How long should my data analyst CV be?
It is UK best practice to create a CV of around 2 A4 pages. CVs over two pages are considered long, and CV’s of over three pages start to become difficult for a recruiter to absorb fully. Evaluate the way you use bullet points and cut out any fluffy language or filler words, making your text as punchy and data-focused as possible.