How to write a data analyst CV (with tips and an example)

By Indeed Editorial Team

Updated 1 December 2022

Published 30 November 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Our world is full of data and the ability to make sense of this data is a skill that many companies desire. This means a role as a data analyst is one that is almost always in demand. If you want to prove that you're the most suitable candidate for the role, knowing how to create a CV for a data analyst can improve your odds of impressing a hiring manager. In this article, we discuss the role of a data analyst, how to write a successful data analyst CV and the responsibilities and skills the role requires.

What is a data analyst and what to consider on your data analyst CV?

To understand how to write a standout data analyst CV, it's important to understand the role. The process of data analysis is gaining insights from data to help inform business decisions. Data analysts collect the data, clean the data, analyse it and interpret the results. There are a number of different forms of data analysis, including:

  • Descriptive analysis: This type of analysis aims to answer the question ‘what happened?'. It works to describe or summarise quantitative data by presenting statistical information.

  • Diagnostic analysis: This aims to answer the question ‘why?'. For example, if a descriptive analysis shows a weird pattern, the diagnostic analysis aims to work out why the pattern occurred.

  • Predictive analysis: This style of analysis uses data to form projections and predictions. For example, you may predict sales that are likely to occur after a big marketing push.

  • Prescriptive analysis: This type of analysis builds off the other types of analysis to create recommendations about how a business can act.

Related: How much does a data analyst make? (With Qualifications)

Elements to include on your CV

Because the role of a data analyst encompasses many different things, this is a role that's usually high in demand. Companies use analysts to understand their business model and to decide what practices to use to make their company succeed. To decide who is the best candidate for a role, they may take a deep look at your CV to determine if you have all the necessary skills to be successful in this role. If you're thinking about a career as a data analyst, here are some steps you can take to write a professional CV that can help you get a job as a data analyst:

1. Choose the format

The format is an important part of your CV, as it structures your information in a way that allows your potential employers to see your work experience and education in a clear manner. For most CVs, the most appropriate format is a reverse chronological format. This displays your information with the most recent experience at the top and the page and the older experience at the bottom.

This allows the hiring managers to see the most relevant information first, which is crucial, as they usually have hundreds of applications to go through. If you have coding experience and want to show off your skills, consider creating an innovative website CV that highlights your skills and experience in a vibrant manner.

2. Write a standout professional summary

Once you choose your format, consider adding a professional summary to the top of your CV. This is a summary that highlights who you are in a concise manner, which the hiring manager may use to decide whether they might consider the rest of your CV. Start by adding a sentence or two about who you are, then add in your education and your experience and knowledge of data analysis. To end, consider adding your professional career goals and how the role you're applying for can help you to achieve these goals.

3. Describe your work experience

Your work experience is the first thing that a hiring manager may look at to determine whether you're the right candidate for the role. Your work experience within the field of data analysis can be on the first page of your CV. Start by filling out the dates you worked, job title and company you have worked for.

Underneath each title, list three or four bullet points that detail your role and the skills you've gained from working in these positions. If you haven't got any experience as a data analyst, use this space to highlight any other experience you have. For example, if you have experience in coding or IT, mention this experience and write about how it helps you to successfully analyse data.

4. Add in your education

Hiring managers want to know that you have the relevant education to succeed in a role as a data analyst. Your CV can highlight any relevant education that highlights your capabilities. Most entry-level positions in data analysis require a bachelor's degree. Here are some of the degrees that employers might be looking for:

  • Data science

  • Computer science

  • Finance/economics

  • Mathematics

  • Statistics

Some companies may also require that you have a master's degree in data analytics or a similar field. You may also want to learn a couple of coding languages. The role of a data analyst relies heavily on computers, so a strong understanding of how computers work can help you to succeed in the role.

Related: How to become a data scientist in 4 steps

5. Note your hard and soft skills

Although your character and your work experience count for a lot, hiring managers also want to know that you have a range of skills that make you well suited to a career in data analysis. Since this is such a technical role, they want to know that you have technical skills.

Here are some of the hard data analyst skills:

  • maths

  • logic

  • analysis

  • data mining and data QA

  • pattern and trend identification

  • business intelligence

  • programming languages

  • data science research methods

The soft skills they use include:

  • communication

  • ability to work as part of a team

  • critical thinking

  • attention to detail

  • time management

  • leadership

Related: Analytical skills: definitions and examples

What are the responsibilities of a data analyst?

Data analysts are responsible for handling a company's data and using this data to find out critical information about the company. They have expertise in data storage structures, data mining and data extraction. Once they extract the data, their role is to transform it into readable statistics that can give insight into various aspects of the company. Once they analyse the data, they may also come up with solutions if any issues are found.

Example CV for a data analyst

If you're thinking about a career in data analysis, here is an entry-level CV that you can use as an example:

Ada James

Professional summary

Hardworking and professional with a particular aptitude for statistical analysis. Completed a degree in Data Science at the University of Bristol with a 1st. Deep understanding of coding languages including HTML and Python. Experience within the IT industry as a customer support engineer for the University of Bristol, with an understanding of data mining, business intelligence and big data analysis. Looking to expand my knowledge of statistical analysis in the field of data analysis.


2015-2019: Customer support engineer, Bristol University Campus

Worked part-time during my degree helping students to set up their technology and troubleshooting issues. Successfully fixed a multitude of complex issues without being overseen.


Bsc in Data science from the University of Bristol Achieved a first with honours and performed better than 80% of the cohort.

Achieved A's in these modules: Statistics and statistical data mining, big data analysis and machine learning.

AAB at A Level in: Mathematics, Economics and Statistics.

Hard and soft skills

  • Adept understanding of maths and statistics: I consistently achieved A's in maths and statistics modules at university.

  • Proficiency with programming; Fluent in coding languages including HTML and Python.

  • Data mining and data QA: Successfully completed a module in data mining.

  • Understanding of Business intelligence: and the desire to further develop this knowledge.

  • Adept communication skills: While working as a customer support engineer, I constantly had to communicate with clients to quickly work and solve their technology issues.

  • A keen eye for detail: With the ability to spot data patterns and irregularities quickly.

  • The ability to work as a team member and individually: During projects at university I regularly led my team members and worked alongside them to create working code.

Please note that none of the companies mentioned in this article are affiliated with Indeed.


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