Data Analyst CV: Example and How to Write [Expert Tips]

Convincing management or other stakeholders to take this or that course of action is one thing. You have the data and your ability to interpret it to guide you. Writing a good data analyst CV from scratch is something else entirely. Or is it?

 

It just so happens that a data-driven CV based around concrete examples and hard statistics is going to be the most effective one. You might not fully know what to aim for, but the good news is that you already have what it takes to hit the bullseye.

 

As a data analyst you already have the most important skills you need to write an effective CV. You just need some sign-posts to focus your efforts in the right direction. This article will supplement your data-wrangling prowess and help get you there.

 

Read on to see how you can use your data analyst skills to write the most effective data analyst CV possible. You’ll find UK-based samples, a template, and a complete walkthrough that’ll have your CV looking at least as impressive as the one below.

 

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Data analyst CV example

 

Emma Sanders

T: 078 4513 1615

E: emma.t.sanders@lcmail.co.uk

LinkedIn: linkedin.com/in/emmasanders9

 

Personal Statement

 

Highly adaptable, plug-and-play data analyst with 5+ years’ experience in the financial, medical, and IT industries. Successfully collated, organised, and analysed over 3 PT of data to date. Recently prepared medical data for completely novel use in training Deep Learning Applications, 7 weeks ahead of schedule and £13,500 under budget. Seeking opportunity to leverage advanced statistical analysis, data management, and quality assurance skills in supporting Vodeki’s continued expansion into the European market.

 

Work Experience

 

Data Analyst

Daltak Ltd, London

March 2017—present

  • Organised case files, maintained accurate research records and recorded close to £2,000,000 in billable costs.
  • Continuously improved the data preparation process through scripting and automation, resulting in a 96% reduction in labour overhead in Week 40 as compared to Week 1.
  • Designed and executed 43 completely bespoke tests of models and software using various CDA and EDA techniques.
  • Ensured that data and models were managed and documented according to Quality Standards and Procedures, maintained a 0% revision rate since April 2019.

 

Graduate Data Analyst

Leyla Group, London

January 2015—February 2017

  • Reconciled various reports on a weekly and monthly basis for an average of 4–6 parallel projects using various data systems.
  • Identified two previously untracked types of anomalies and corrected the resulting data issues, resulting in 25-35 additional, potentially significant anomalies being automatically caught for every 10,000 data points cleansed.
  • Produced management information reports on a regular basis and developed new processes that improved efficiencies by no less than 10% and in one particular case over 25%.
  • Carried out ad-hoc and project analysis to identify and rectify issues caused by the production of information, reducing redundant computational resource load by up to 43%.
  • Delivered findings of analysis to key stakeholders and clients on regular basis, effectively logging over 100 hours of presentation time and submitted in excess of 1,000 pages of reports and summaries.

 

Education

 

MSc Data Analytics, 2016–2018 (part-time)

Queen Mary University of London

 

BSc Business Information Systems (2:1), 2011–2014

Middlesex University London

 

A-levels: Business Studies, Economics, English, 2009–2011

Hedgelane High School, London

 

9 GCSEs (including Mathematics and English), 2007–2009

Hedgelane High School, London

 

Skills

 

  • Communication skills: presented findings to a wide variety of key stakeholders during the course of weekly and monthly meetings
  • Data management: implemented new data storage techniques borrowed from the medical industry and created custom organisation protocols
  • Data modelling, data cleansing, and data enrichment skills: independently lead a team of specialists through the establishment of new data processing procedures
  • Data visualisation: frequently used Tableau and similar programs to create clear visual representations for non-technical stakeholders
  • Project management skills: worked with stakeholders to gather requirements and deliver findings
  • Quality assurance, validation and data linkage skills: ensured that data and models were managed and documented according to Quality Standards and Procedures
  • Statistical methods and data analysis skills: strong academic and professional grounding in advanced statistical and data analysis methods

 

Software Proficiency

 

  • Statistical programmes, including: SPSS, SAS, and RapidMiner
  • Programming environments: R, Python, and MATLAB
  • Relational databases, including MS Access
  • Querying databases using T-SQL, SQL Server
  • Extensive MS Excel knowledge (including pivot tables, VLOOKUP)

 

Now you know what a perfect CV looks like. Here’s how to write a data analyst CV:

 

1. Write a data-rich personal statement and put it at the top of your data analyst CV

 

Your CV personal statement has to do three things. It has to:

  • Introduce you as a data analyst
  • Give some indication of what you can bring to the company
  • Show that your employment goals line up with the company’s goals.

 

And it has to do all this in just 3–4 sentences and 50–150 words.

 

Introducing yourself is as simple as stating how many years’ experience you have an in what industries or niches. You can demonstrate the kind of value you can bring to the company by showcasing one of your achievements. Finally, give an honest account of your goals, but focus on the benefits to the company.

 

If you’re more experienced and applying for a senior data analyst job, then you can double up on your achievement. In this case, you’ll end up with 4–5 sentences instead of 3–4. If you’re stuck, then simply answer each of the following questions.

 

  1. What kind of data analyst are you? How long have you been working?
  2. Which industries have you worked in?
  3. What’s your most impressive (relevant) achievement?
  4. What’s your most unique achievement? (Optional)
  5. What are you hoping to achieve in this job? (Achieve for your employer, not yourself).

 

This might be obvious to someone in your line of work, but do keep in mind that more and more employers are using Applicant Tracking Systems (ATSs). Seed your personal statement with keywords from the job advert and be sure to mention the name of the position and the company in your statement.

 

The personal statement comes first in your CV but it’s best written last. It’ll be much easier once you have your work experience and skills sections finished. 

 

Data analyst CV personal statement example

 

Highly adaptable, plug-and-play data analyst with 5+ years’ experience in the financial, medical, and IT industries. Successfully collated, organised, and analysed over 3 PT of data to date. Recently prepared medical data for completely novel use in training Deep Learning Applications, 7 weeks ahead of schedule and £13,500 under budget. Seeking opportunity to leverage advanced statistical analysis, data management, and quality assurance skills in supporting Vodeki’s continued expansion into the European market.

 

A strong CV summary will convince the recruiter you’re the perfect candidate. Save time and choose a ready-made personal statement written by career experts and adjust it to your needs in the LiveCareer CV builder.

 

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2. Let the data inform your data analyst CV work experience section

 

Use a chronological format to organise your data analyst work experience, starting from your most recent position and working your way back from there. A chronological format is best as it’s what hiring managers will expect to see in a data analyst CV. It’s also going to be more easily parsed by an ATS.

 

The key to making each CV job description truly data-driven and padding-free is to quantify every single bullet point. If you can’t quantify something, then it has to make way for something that can be quantified. This automatically shifts the emphasis away from duties and onto achievements.

 

Use accomplishment statements and something like the Action + Problem/Project = Result (APR) formula to structure your bullet points. List up to six accomplishments under each job description. Use this template for the headings:

 

[Job Title]

[Company Name, Location]

[Dates of Employment]

 

If you’re writing a senior data analyst CV, then avoid leaving redundant points in older job descriptions. If you have similar accomplishments listed for two different positions, then keep only the more recent one. Be merciless when cutting down on this kind of repetition. Consider it a dataset cleanse.

 

Writing a graduate data analyst CV, junior data analyst CV or applying for a data analyst apprenticeship? Then remember to include any internships, placement work, and volunteer work you’ve done and put this section after your education section.

 

If you have no experience at all and no other sources for relevant accomplishments, then give this CV section a miss altogether and consider writing a student CV instead.

 

Data analyst CV job description samples

 

Data Analyst

Daltak Ltd, London

March 2017—present

  • Organised case files, maintained accurate research records and recorded close to £2,000,000 in billable costs.
  • Continuously improved the data preparation process through scripting and automation, resulting in a 96% reduction in labour overhead in Week 40 as compared to Week 1.
  • Designed and executed 43 completely bespoke tests of models and software using various CDA and EDA techniques.
  • Ensured that data and models were managed and documented according to Quality Standards and Procedures, maintained a 0% revision rate since April 2019.

 

Graduate Data Analyst

Leyla Group, London

January 2015—February 2017

  • Reconciled various reports on a weekly and monthly basis for an average of 4–6 parallel projects using various data systems.
  • Identified two previously untracked types of anomalies and corrected the resulting data issues, resulting in 25-35 additional, potentially significant anomalies being automatically caught for every 10,000 data points cleansed.
  • Produced management information reports on a regular basis and developed new processes that improved efficiencies by no less than 10% and in one particular case over 25%.
  • Carried out ad-hoc and project analysis to identify and rectify issues caused by the production of information, reducing redundant computational resource load by up to 43%.
  • Delivered findings of analysis to key stakeholders and clients on regular basis, effectively logging over 100 hours of presentation time and submitted in excess of 1,000 pages of reports and summaries.

 

3. Include an education section in your data analyst CV

 

Once you have an undergraduate degree and some work experience, your high school education won’t be relevant any longer. The exception here is if you’re writing a graduate data analyst CV with little or no experience.

 

When adding university degrees, include the name of your degree, the years you attended (with an expected graduation date if you’re still studying), the degree class (e.g. 2:1), and the name of the awarding institution and its location.

 

If you do include your high school education, then list all of your A-levels by subject name, the years during which you studied them, the name of the school and its location. For GCSEs simply state how many you completed, the years over which you completed them, the name of the school and its location.

 

If you mention your GCSEs at all, then mention Mathematics and English when stating how many you completed. Employers often need to see that you have passed in Maths and English and highly developed numeracy and communication skills are definitely must-haves for a data analyst at any level.

 

If you’ve just graduated from university and don’t have much experience yet, then include this section above your work experience section. You may also want to add bullet points here to highlight your achievements while studying (for example areas of exellence or extracurricular activities). Use accomplishment statements just like in the work experience section.

 

Data analyst CV education section example

 

MSc Data Analytics, 2016–2018 (part time)

Queen Mary University of London

 

BSc Business Information Systems (2:1), 2011–2014

Middlesex University London

 

A-levels: Business Studies, Economics, English, 2009–2011

Hedgelane High School, London

 

9 GCSEs (including Mathematics and English), 2007–2009

Hedgelane High School, London

 

 

4. Add a skills section to your data analyst CV

 

Data management is an unavoidable part of data analytics. It’s also a skill-set that’s going to come in handy when describing your skill-set. Start by setting out a simple database of data analyst skills. A spreadsheet will be more than enough for this.

 

Brainstorm as many data analyst skills as you can. The more the better. Now comes the tricky part: add a sentence to each skill that demonstrates that you possess that skill. Be concrete and specific and quantify if possible. If you can’t back a skill up in this way, then purge it from the list.

 

This spreadsheet is now your master list of data analyst skills. Organise it however you like, make it searchable in 17 different ways if you feel the need. Add to it as new skills and examples occur to you over time.

 

Now, back to your data analyst CV. Bring up the advert for the job to which you’re applying. Find the skills on your list that match the ones on theirs. Make sure you at least have a skill for each one they require, and a total of 5–10. Aim for a mix of hard, soft, and IT skills.

 

Do this for each job application, always tailoring your skills section to the given job advert. If the advert uses keywords synonymous with the ones in your list, then swap them out—just in case an ATS is scanning your CV for relevance.

 

Data analyst CV skills section example

 

  • Communication skills: presented findings to a wide variety of key stakeholders during the course of weekly and monthly meetings
  • Data management: implemented new data storage techniques borrowed from the medical industry and created custom organisation protocols
  • Data modelling, data cleansing, and data enrichment skills: independently lead a team of specialists through the establishment of new data processing procedures
  • Data visualisation: frequently used Tableau and similar programs to create clear visual representations for non-technical stakeholders
  • Project management skills: worked with stakeholders to gather requirements and deliver findings
  • Quality assurance, validation and data linkage skills: ensured that data and models were managed and documented according to Quality Standards and Procedures
  • Statistical methods and data analysis skills: strong academic and professional grounding in advanced statistical and data analysis methods

 

5. Boost your data analyst CV with additional sections

 

This is where you can carve out a space to showcase your CCA Data Analyst, MCSE: Data Management and Analytics, or eCAP chops. Additional certifications and qualifications like these can plug the gaps left by a university education, especially if you’re background isn’t directly in data analytics.

 

Add any other sections that will give a fuller picture of you as an analyst. Speak any languages other than English? This can be a real advantage, even if you work in a purely English-speaking environment. Include the languages you speak and the level at which have a grasp of them.

 

You could also add any hobbies, awards or non-work related achievements that might be relevant to your performance as a data analyst, even if not directly. Above all, don’t overlook the opportunity to fill out the data-set that is your CV.

 

Data analyst additional section example

 

Software Proficiency

 

  • Statistical programmes, including: SPSS, SAS, and RapidMiner
  • Programming environments: R, Python, and MATLAB
  • Relational databases, including MS Access
  • Querying databases using T-SQL, SQL Server
  • Extensive MS Excel knowledge (including pivot tables, VLOOKUP)

 

6. Write a data analyst cover letter to go with your CV

 

Unless you’ve been explicitly asked not to do so, you should always submit your CV with a cover letter. It’s SOP and generally expected, regardless of how closely your cover letter will be read, if at all. Of course, it’s always better to have a great cover letter that’s never read than a bad one that is.

 

Use the standard UK cover letter format, including a properly set-out header, salutation, and sign-off. The body of your cover letter should come in three parts:

  • An attention-grabbing opening paragraph—between 60 and 80 words
  • A showcase of skills and achievements—between 120 and 200 words
  • A strong closing paragraph and CTA—between 40 and 60 words.

 

What about the length of your cover letter? It should be over half an A4 page, but no longer than a single A4 page, about 200-350 words in total. 

 

7. Format your data analyst CV appropriately

 

Be mindful of the following basic CV formatting rules:

 

Proofread and spellcheck your data analyst CV and cover letter. Then proofread it again. Use any of the apps, web apps, or programmes out there that can help you with spelling and grammar. No one will notice that you don’t have any mistakes, but they will definitely notice if you do.

 

One last thing: follow up if you haven’t heard back after a week. Fire off a quick email or pick up the phone and ask, it shows that you’re serious about your application and will give you some indication of how your application is going.

 

You don’t have to be a CV writing expert. In the LiveCareer CV builder you’ll find ready-made content for every industry and position, which you can then add with a single click.

 

Create your CV nowcv builder

 

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LiveCareer Editorial Team
LiveCareer Editorial Team

About the author

Since 2005, the LiveCareer Team has been helping job seekers advance their careers. In our in-depth guides, we share insider tips and the most effective CV and cover letter writing techniques so that you can beat recruiters in the hiring game and land your next job fast. Also, make sure to check out our state-of-the-art CV and cover letter builder—professional, intuitive, and fully in line with modern HR standards. Trusted by 10 million users worldwide.

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