A data analyst CV needs to do more than just list your past experiences or education. It should highlight measurable achievements, technical expertise, and data-driven projects that prove your value to employers. By aligning your CV with industry standards and showcasing results, you can stand out in a competitive job market.
In this article, we will show you how to write a data analyst CV, give you an example for inspiration, and list which mistakes you should avoid.
Key Takeaways
A data analyst CV should highlight technical skills, measurable achievements, certifications, and projects.
When writing a data analyst CV, make sure to include a personal statement, skills section, relevant work experience, projects, and portfolio links.
You can make your CV ATS-friendly by including job-specific keywords, using standard headings and readable fonts, avoiding complicated graphics, and saving the file in a commonly used format.
Common mistakes when writing a data analyst CV include listing irrelevant skills, omitting projects and portfolio links, formatting the document poorly, failing to add measurable results, and creating a generic summary.
What Employers Look For in a Data Analyst CV
In a data analyst CV, employers look for relevant technical and soft skills, measurable achievements, and certifications that prove your expertise in particular areas.
However, the most important aspects are measurable achievements, which show your experience and capabilities, and technical skills, which demonstrate your job-specific knowledge.
In addition, projects and certifications can significantly boost your chances, as they highlight your practical experience and your willingness to take an extra step to further your career.
Data Analyst CV Example
The following data analyst CV example demonstrates how to write a compelling document to help you secure a job in this field.
So, let’s take a look:
How to Write a Data Analyst CV: 7 Tips for Success
To write a data analyst CV, you should include the most important sections, such as a personal statement, skills, work experience with measurable achievements, and relevant projects. Furthermore, keep your CV clear and concise, tailor it for each new application, and update it regularly as you gain new experience.
Now, let’s examine these tips more closely:
#1. Start With a Strong Personal Statement
A strong personal statement provides your potential employer with a quick insight into your professional background, skills, and most significant achievements. Essentially, it’s an overview of your CV, so it can serve as a hook and convince recruiters to read the rest.
As a result, your personal statement must be attention-grabbing, effective, and highly informative. However, since your space is severely limited, each word should count; you only have 4–5 lines to summarise yourself as an employee.
Here’s what a good data analyst CV summary should look like:
CV Summary Example
Results-driven data analyst with 10 years of experience in translating raw data into meaningful insight for businesses. Developed an automated reporting dashboard that led to a 40% decrease in monthly reporting time and improved accuracy across all business units. Known for my excellent data-driven strategies, focus on efficiency, and critical thinking skills.
#2. Highlight Relevant Technical Skills
Your relevant skills should be highlighted throughout your CV: in the personal statement, skills section, and work experience section. This means all skills, soft ones included, but as a data analyst, you should focus on your technical skills.
In other words, you should list the tools and software you can use, as well as the specific competencies that only people in your field possess. For instance, data analysts must be excellent at data management and visualisation, so these skills should be incorporated.
Here is how you can include data analyst skills on your CV:
Data Analyst Skills Example
Skills
SQL
Python
Excel
Tableau
Power BI
R
Data analysis
Data visualisation
Database management
Machine learning
Data cleansing
Critical thinking
Problem-solving
Communication
Presentation skills
#3. Emphasise Measurable Achievements
Next, you should emphasise your achievements in your CV, usually by adding them to the bullet points in the work experience section. Pick out 3–6 accomplishments for each entry, briefly explain them, and include measurable results to support your claims.
Now, this might be more difficult to do on CVs for data analysts with no experience, as they might not include the work experience section at all. In this case, you may talk about your achievements in the education section instead, following the same approach with bullet points.
This is how you can talk about your measurable achievements:
Measurable Achievements Example
Work Experience
Data Analyst ABC Agency London, UK August 2021–March 2024
Conducted a thorough analysis of customer purchase data, which resulted in a 15% sales increase.
Automated reporting workflows in Python, reducing manual reporting time by 50%.
Designed SQL queries for data extraction and transformation across multiple databases.
#4. Include Relevant Projects and Portfolio Links
Including projects and portfolio links gives you an edge over other candidates because it demonstrates your practical experience and problem-solving abilities. This is especially relevant for entry-level candidates, who can compensate for their lack of experience with the projects they’ve worked on.
Here’s how you can include this section on your data analyst CV:
Relevant Projects Example
Projects
Customer Churn Analysis (Python, SQL)
Stock Market Trends (Python)
#5. Keep the CV Clear and Concise
Your CV shouldn’t be longer than two pages, with one being ideal if you’re an entry-level candidate. The only exception is senior-level roles; you can write 2–3 pages in this case, especially if you have plenty of work experience. However, be sure to include only relevant information and avoid going overboard.
Furthermore, remember to keep the information clear and well-organised, so that it’s easy to read and follow. Recruiters often scan the document briefly before going into details, and its overall appearance and format can significantly affect their perception.
So, make sure to use professional fonts, headings for each new section, bullet points, and paragraphs. Keep in mind that white space can be just as important as the text; your document needs a good balance between the two to be readable.
#6. Tailor Your CV for Each Application
One-size-fits-all isn’t a good approach when it comes to writing CVs, as it results in a generic document that doesn’t meet the employers’ requirements. After all, even when you’re applying for jobs in the same field, some aspects will be different, so your application must be, too.
That doesn’t mean you should write a brand-new CV from scratch for each position. However, once you create one, remember to customise it for each application, even if that means just adding a few skills or focusing on different achievements in your work experience section.
#7. Proofread and Update Regularly
Once you finish writing your CV, go over it once again to check for any mistakes, typos, and grammatical errors. Leaving any glaring issues like that can come across as highly unprofessional and significantly lower your chances of securing an interview.
Also, remember to update your CV regularly with new skills and experiences. That also means removing outdated competencies that are no longer relevant for your industry and replacing them with those that are.
How to Make Your Data Analyst CV ATS-Friendly
To make your data analyst CV ATS-friendly, you can do the following:
Use standard headings, such as work experience, education, and skills; being overly creative in this aspect can actually hurt your chances.
Include job-specific keywords from the job description to ensure the scan picks up on them and flags your CV as a potential match.
Avoid graphs and complicated designs that may confuse the scan. It’s best to stick to simple, tried-and-true formats, even if they aren’t particularly creative.
Use readable fonts, such as Calibri, Times New Roman, or Helvetica. The scan is unlikely to decipher overly stylised fonts correctly.
Save the file in a standard format, like .docx or PDF, to ensure it’s compatible with the scan and can be easily parsed.
5 Common Mistakes to Avoid in Your Data Analyst CV
Common mistakes to avoid in your data analyst CV include the following:
Data Analyst CV Mistakes
Listing too many irrelevant skills. Many candidates mistakenly believe that including more skills leads to better results, but this isn’t necessarily the case. If most of the skills you add to the list are irrelevant to the position you’re applying for, then they will be just filler, which recruiters don’t typically appreciate.
Lack of measurable results. Measurable results give weight to your claims and specificity to your CV. You don’t have to force yourself to include them in every bullet point, but don’t entirely leave them out either.
Typos or poor formatting. Typos and poor formatting make your CV look unprofessional at first glance, so you must double-check whether everything’s in order before submitting it. Use a grammar checker for proofreading and read about the best CV formatting practices; this will ensure your document looks clean and presentable.
Generic professional summary. Your professional summary should be as specific as possible, tailored to the job you’re applying for. Don’t just vaguely mention your skills, job title, or future goals; illustrate your point with concrete examples of achievements and measurable results.
Omitting portfolio links or project examples. The best way to demonstrate to your employer what you’re capable of is by giving them access to your portfolio and projects. If you don’t, they have less information to go on, which can backfire if you’re competing against strong candidates.
Create a Professional CV Within Minutes
If you need additional help writing a data analyst application, use our CV builder to create one within minutes. All you need to do is follow the instructions, enter the required information, pick a CV template, and finalise the document. After that, you can submit it directly to your potential employer.
And if you want to write your application by yourself, consider browsing through our CV examples for inspiration. We offer an extensive selection for various professions, so you’re sure to find what you need in our collection.
Final Thoughts
With our guide, you should be able to write a compelling data analyst CV in no time and impress recruiters. Just remember to focus on measurable achievements, include projects and portfolios, and add relevant technical skills; these aspects are crucial for a data analyst.
Data Analyst CV FAQ
#1. What are the top 3 soft skills for a data analyst?
The top 3 soft skills for a data analyst are problem-solving, critical thinking, and attention to detail. These are crucial for effectively interpreting data and using it to identify trends and patterns.
#2. What is a good summary for a data analyst?
A good summary for a data analyst CV should include your work experience, the most significant achievement accompanied by measurable results, and key soft and hard skills. However, the summary can’t be too long (4–5 lines at most), so you need to make sure every word counts.
#3. Do I need to list all software tools I know?
You don’t need to list all software tools you know, but you should include the most relevant ones, such as SQL, Python, Microsoft Excel, and Tableau. Other tools can be a bonus, though be careful not to overdo it; your application shouldn’t be too long or stuffed with insignificant details.
#4. Should I include freelance or internship experience?
You should include freelance or internship experience, especially if you’re just starting your career. These can make your application stand out even without significant work experience. However, as you progress in your career, you’ll likely have more impressive entries to list, so internships and freelancing might not make the cut.
#5. Is data analyst still a good career in 2025?
Data analyst is still a good career in 2025; in fact, it remains in high demand and is well-paid. Considering the importance that data plays in most professions nowadays, we don’t expect this to change anytime soon, so you shouldn’t hesitate to pursue it.
