Creating a data analyst CV requires clearly highlighting technical expertise, the business value added, and the ability to interpret complex data.
Fill your CV with the appropriate tools (be they SQL or Python), measurable achievements, and industry jargon so that you pass ATS screens—and be sure to develop a CV and data analyst cover letter that work together in conveying your story.
From this guide, you will learn how to:
- ↪ Create a suitable data analyst CV from scratch
- ↪ Highlight the appropriate skills, tools, and qualifications
- ↪ Tailor the CV based on the job specification
- Format the CV for ease of reading and ATS compatibility
- ↪ Draw on 33 tested CV samples for ideas
- ↪ Avoid common interview pitfalls that cost you opportunities
Why this CV works
- Since mid-level roles occupy a position that is neither junior nor senior, it’s essential to highlight and demonstrate every aspect of your experience in your data analyst CV. If you have a specific company or niche in mind, pay particular attention to the individual job description, looking for keywords, the mission statement, and even the company culture.
- Once you know what the employers are looking for, you can include directly applicable keywords and matching language in your work experience bullet points (provided the keywords truly describe you!).
Why this CV works
- When beginning with your junior data analyst CV, you might feel like your professional history looks a bit sparse—but that’s alright! You can enhance your credibility and add interest by mentioning an impressive project you might have undertaken.
- Examples and measurable metrics of success can make any CV stand out from the competition: And they’re especially important for junior data analysts!
See more junior data analyst CVs >
Why this CV works
- As a senior data analyst, the need for a comprehensive career objective diminishes. Your senior data analyst CV should heavily focus on work history, excellent KPIs, and leadership.
- With experience comes a whole host of skills; however, don’t list every ability you have in your CV skills section.
See more senior data analyst CV >
Why this CV works
- If you lack paid work experience in a field, consider it an opportunity to include personal projects on your data analyst intern CV where you demonstrated skills relevant to the position you’re applying for.
See more data analyst intern CVs >
Why this CV works
- Your career choices might not always reflect what you chose to study at university. But when that is the case, you should let a recruiter know!
- For instance, if you’ve completed a BSc in Health Care Informatics, it means you’ve spent a considerable amount of time learning how to analyse and interpret healthcare data and information—which is what most recruiters will be looking for in your healthcare data analyst CV.
See more healthcare data analyst CVs >
Why this CV works
- Combining marketing and data analysis requires a lot of skill, and we know it can be hard to convey all your credentials on your marketing data analysis CV. That’s where you can include relevant certification to do the talking for you.
- For instance, if you’ve completed a Professional Certified Marketer (PCM) certification, it’s clear you understand the nuances required to be a marketing professional.
See more marketing data analysis CVs >
Why this CV works
- Analytics management is complex and highly technical, but your data analytics manager CV needs to make your credentials and qualifications easy to understand.
- Making your CV easy to read can be as simple as using a CV template, but it also means condensing your bullet points as much as you can, including metrics, and core skills to boost your credibility quickly, and leaving just enough white space to make it a breeze to skim.
See more data analytics manager CVs >
Why this CV works
- Use a CV format that includes the work experience in reverse-chronological order.
- By doing so, you place your most recent achievements at the very top of your business data analyst CV—which is where a recruiter or your next potential employer is going to look first.
See more business data analyst CVs >
Why this CV works
- Even a candidate with years of industry experience can optimise their clinical data analyst CV with an additional qualification. Make sure recruiters can easily see that you’re a Certified Clinical Data Analyst (CCDA) to add an extra level of credibility to your background.
Why this CV works
- As much as your technical skills are vital to the success of your data governance analyst CV, you need more than that for your application to stand out. Take Aisha’s piece, for example, and see how she highlights her ability to save time and money.
- For your situation, achievements that involve cutting costs (cue: saving 13% in infrastructure expenses) and reducing process times (cue: saving 12 hours per week) would significantly enhance your chances of early success.
Why this CV works
- Your SQL data analyst CV needs to be impressive, regardless of how much experience you have. Even if you only have placements, choose your work experience bullet points wisely.
- Start by analysing the requirements in data analyst job descriptions to get an idea of what employers require.
See more SQL data analyst CVs >
Why this CV works
- If being good with numbers under pressure is your thing, then you’re already halfway there! All that’s left is to sprinkle your financial data analyst CV with some clear work metrics.
- List the top financial institutions such as Citi and Deloitte that you’ve worked for, and you’ll be on every employer’s recruitment list.
Why this CV works
- You may try, but a data quality analyst CV without a solid backing of tools and software competencies will be shunned by recruiters.
- Therefore, to ensure a smooth process for your application, don’t omit any skills that you feel are relevant to the job—either in your CV or by letting an AI cover letter generator have a go at your application.
Why this CV works
- The Standout template is essentially your best friend as it allows you to add your education, skills, and work experience without squeezing everything in. And to ensure that you’ve included all the essential sections, parse your CV and add anything you may have missed.
Why this CV works
- Here’s an eye-opener: recruiters will scan CVs for a mere 6 seconds. Outrageous, right? Even if your Python data analyst CV happens to be the final piece on the stack and the hiring manager has all the time in the world, drawing attention to your success record of delivering results can increase your appeal.
Why this CV works
- Enhance your Power BI data analyst CV by emphasising any Power BI-based tasks you’ve led and the impact they had on the company.
Why this CV works
- Unsure how to make a CV? No problem! Start by using a solid CV outline to help you get a feel for what a CV looks like, then add your experience and skills one at a time.
- As an aspiring professional, you’ve got options for showcasing your skillset on your entry-level data analyst CV.
See more entry-level data analyst CVs >
Why this CV works
- A vast majority of hiring managers are keen on candidates who are fully committed, which is why we’re all for highlighting relevant certifications in your Snowflake data analyst CV.
- For example, holding a SnowPro Advanced Data Analyst certification indicates that you are committed for the long term and are already equipping yourself with the skills and knowledge to complete the journey.
Why this CV works
- Three solid years—that’s how long you’ve been analysing data and helping your employers solve problems. However, letting the recruiter know that is not enough to get you the job. You need a data analyst CV with 3 years’ experience demonstrating you can actually make things happen to even stand a chance.
Why this CV works
- Bullet points in an Excel data analyst CV can be lengthy, so ensure you have the right amount of spacing between points to avoid giving employers an eye test.
- Use the Standout template from our library to ensure you have ample space to include all your skills, experience, and education.
Why this CV works
- Don’t send your HR data analyst CV out the door without adding impactful numbers like “Formulated 2 advanced Microsoft Excel models… saving £13,941 in additional talent acquisition costs.” This way you’re not just discussing data, you’re demonstrating its monetary value!
Why this CV works
- Trust us; nothing screams “Unfit applicant” louder than an avalanche of grammatical mistakes in your freelance data analyst CV. Follow Corbin’s lead and go through your sales pitch with a fine-tooth comb, eliminating any oversights that could ruin your job prospects.
- After thorough proofreading, you can rid your masterpiece of those nasties using an AI-powered CV fixer.
Why this CV works
- If you really want to win over the potential employer, you must prove you’re a well-rounded professional. That should make showcasing soft skills in your Microsoft data analyst CV a top priority.
Why this CV works
- Who says all data analysts have to start out as programmers? Even a modest beginning such as a data entry clerk is a valuable addition to your R data analyst CV if it’s relevant.
- Follow John’s example and explain how you’ve worked with data, particularly (very important!) on R-based frameworks.
Why this CV works
- The career objective on your AWS data analyst CV gives you a bit of space to discuss your professional skills and aspirations and how they align with the role you’re applying for.
- You only have a couple of sentences to do so, but it’s still a powerful way to tell a recruiter exactly what you bring to the table and what they stand to gain if they employ you.
Why this CV works
- When interpreted with a keen eye, big data can yield surprisingly impactful insights for companies. However, to make a difference, these inferences need to be communicated clearly.
- Elevate your big data analyst CV by showcasing your expertise in delivering actionable analyses to key organisational stakeholders.
Why this CV works
- Your Alteryx data analyst CV will benefit from incorporating clear examples of your work with abstract skills.
- For example, instead of just listing your proficiency in data manipulation, explain how you utilised your expertise in this area to free up storage space and increase efficiency for the organisation.
Why this CV works
- A lead data analyst CV can’t rely on vague claims despite having an extensive professional background—you need to provide evidence! Use concrete, quantifiable metrics like improvement rates and pound-amount company savings to support your achievements.
See more lead data analyst CVs >
Why this CV works
- As a creative data analyst, your ability to help numbers tell a story distinguishes you. Highlight your dual skill set in analysis and visualisation by demonstrating how data and marketing intersect in your past experience.
Why this CV works
- The first thing recruiters/hiring managers will notice when reading your revenue reporting data analyst CV is whether the essential qualifications to perform the job adequately are present.
- Your CV should be tailored specifically to target the list of requirements from the company job description.
See more revenue reporting data analyst CVs >
Why this CV works
- Including a CV summary on your senior insurance data analyst CV helps you highlight your extensive career with quantifiable data from various sources and can help you secure an interview by setting you apart from less experienced competition!
Why this CV works
- As a data analyst, you examine every aspect of how a company operates to enhance business, but how do you demonstrate that you’ve significantly advanced their progress?
- Including numbers and percentages is the quickest way to demonstrate your qualifications to employers. Use metrics to highlight how you’ve made significant improvements whenever possible.
Why this CV works
- Your entry-level risk adjustment data analyst CV needs to demonstrate your skills in data gathering, interpretation, and presentation competencies. To gain that kind of experience, you can approach it in a couple of ways: either through scalable projects or a relevant work placement.
How to Write a Data Analyst CV
Creating the perfect data analyst CV involves highlighting technical skills, problem-solving abilities, and the value added from data insights.
Summary
Optimise your data analyst CV by aligning skills, experience, and formatting with employers’ expectations to pass ATS filters and impress hiring managers.
The key points within the data analytics CV will be the appropriate tools, quantifiable achievements, and industry terms specific to the role.
Let’s cover the following so that you can create the top data analyst CV:
- Which hard and soft skills to emphasise
- How to structure your CV from top to bottom
- Importance of metrics and use of results
- How to tailor your CV to each job application
- How to most effectively prepare your CV for applicant tracking systems (ATS)
- How to stand out and pitfalls to avoid
Showcase your data analyst skills
First, you need to demonstrate that you have the right skills for the job! This means you need to achieve two objectives with the skills section of your data analyst CV. First, you must be able to get past the automatic keyword filters in the applicant tracking system (ATS), which companies use to filter candidates. Next, you want to showcase your technical proficiency to the person reviewing your CV.
If you’re unsure which skills to include, we analysed the data to find out which skills are most in demand for companies hiring data analysts.
These two goals are, unfortunately, mostly at odds with each other. If your aim was simply to get past the ATS, you’d list every skill to get your foot in the door:
Poor—steer clear of a lengthy list of generic skills
The problem? This method is a major warning sign to technical hiring managers. You should only include skills you’d be comfortable discussing in your data analyst interview. Plus, your skills section shouldn’t occupy more than 20 per cent of the page.
Any reasonable employer won’t expect you to know SAS, R, *and* Python. Instead, just list the technical skills that you’ve programmed in before. And avoid including a skill like “data mining” or “data analysis” since a technical hiring manager knows these are just vague terms that don’t mean much. Instead of “data mining,” list actual techniques you’ve used, like “decision trees” or “logistic regressions.”
But even when narrowing it down, sometimes your skills list looks cluttered and difficult to read. Don’t worry; there are several ways to organise your skills section! To begin with, you can list your skills by how proficient you are with them (“Advanced” vs. “Familiar,” for example). Alternatively, you can list your skills by type. You can even mention the years of experience you have with each tool.
For programming languages, mention the libraries and frameworks you use for data visualisation and manipulation in that programming language.
Good—specific skills and modelling techniques
Your data analyst objective
Most of the time, you don’t need to include a CV objective or CV summary; a mistake many data analysts make. If a CV summary or objective doesn’t add value to your application, it’s fine to leave it out.
Here’s a sample data analyst CV objective that you would want to leave off your CV:
Poor—uninformative CV objective
Why include this? It’s unnecessary. If you’ve already shown in your CV that you’ve used tools like Python and SQL to transform data into actionable insights, then your objective doesn’t provide the person reviewing your CV with any new information.
When to include a CV objective
- You’re undergoing a career change. You might say something like, “After 4 years of working in operations, I am now looking to utilise my expertise in transforming data into insights as a data analyst.”
- You have a particular interest in a specific role or company that makes you an especially strong candidate. For example, “Reading Match.com’s data blog sparked my pursuit of a career in data analytics, and I want to further contribute to the data-driven culture at the company.”
Summaries are similar, but they’re for candidates with over 10 years of experience and can include more personal achievements. There isn’t much difference between a CV summary or an objective; all you need to know is when you should include them.
Data analyst CV format
Your data analyst’s CV structure must capture the attention of recruiters and make it easy to find the most important information that highlights your value.
Here are some quick tips for formatting your data analytics CV:
- Keep it to one page.
- Break up bullet points into small, digestible pieces of information.
- Avoid using “I” or “we,” as it occupies space and appears unprofessional.
- Double, triple, and quadruple-check your grammar and spelling. One error can send your CV into the “no” pile!
- Each bullet point on your CV should be a self-contained, complete thought.
When a hiring manager reviews over 50 CVs for a given role, they quickly look for reasons to say “no.” By using these CV-formatting tips, you make it easier for the hiring manager to see your worth and invite you for an interview, bringing you one step closer to a job.
Contact details
Of all the places to make an error, your contact information is the worst place for it to happen. One of our team members recounted their early days after university as a data analyst. When they were applying for jobs, they accidentally wrote the wrong email address on their CV for seven different positions.
Even if they were perfectly qualified for the role, there was no way to contact them because of a minor mistake. So believe us when we say you need to triple-check this section for any spelling, grammar, or link errors.
As part of your contact information, you should include your name and the position you’re applying for (even if it’s not your current position).
You don’t need to include your full address in this section, but you should list your town or city and postcode. You also need your phone number just in case your employer prefers that method.
Finally, include a link to your LinkedIn profile and anything else that might demonstrate why you’re an excellent data analyst. If you have an active GitHub, include a link to that. If you frequently participate in Kaggle competitions, include a link to your profile. Do you have a personal blog where you discuss election data? Make sure to include a link.
Data analyst projects for your CV
If you’re just starting out and looking for your first full-time position, including projects on your data analyst CV is absolutely essential. However, the more work experience you gain, the less critical projects become. By the time you have over four years of experience in the field, you should only include a project of which you’re exceptionally proud.
What projects should you list? Anything where you identified (or were given) a problem and you used data to come up with a solution to that problem. It’s fine if it’s a course project, but it’s even better if you took the initiative yourself.
If you don’t have any such projects, now is the time to work on some. Do you have a question you’ve never answered? An experiment you’ve been longing to try? Think of a way to gather and analyse data to satisfy your curiosity.
Here’s an example: one of our founders had a hunch that the major job boards (Indeed, Glassdoor, and LinkedIn) essentially had the same jobs for data science roles. So, he manually collected data, analysed it, and wrote about it to determine the best job board for data scientists.
The projects you include don’t need to be exhaustive or groundbreaking. Employers just want to see that you can ask a question, use data to answer it, and present your findings reasonably and clearly.
Good—demonstrate that you can answer your own questions with data
When discussing your projects, here’s how you should present what you did:
- Clearly state the question you were answering or the problem you were trying to resolve
- Indicate which tools or languages you used to solve the problem
- Explain the impact of the work you carried out
Your projects section is also an opportunity to provide more context around the programming languages and libraries you listed in your “skills” section.
Use action verbs to make your data analyst CV stand out
When recruiters review your work bullet points, they expect impact, not just a list of duties. It’s your opportunity to use dynamic verbs to showcase your technical expertise, analytical skills, and business acumen.
Here’s how the right action verbs enhance a basic bullet point:
❌ Responsible for analysing data trends to improve sales.
✅ Identified and interpreted data trends, increasing quarterly sales by 19%
Top action verbs for a data analyst CV:
Use these verbs to emphasise achievements—not responsibilities—and pair them with measurable results wherever possible:
- Analysed
- Extracted
- Modelled
- Forecast
- Visualised
- Designed
- Automated
- Interpreted
- Optimised
- Presented
- Developed
- Audited
- Cleaned
- Synthesised
- Validated
Education
Similar to the “projects” section, the education section of your CV will be longer for entry-level data analysts compared to more experienced data analysts. You should include relevant courses you took at university related to data analytics for entry-level data analysts.
Courses relevant to data analytics include any mathematics, statistics, programming, and economics modules you studied. To be an effective data analyst, you need to apply the principles you learned in these modules to real-world problems and datasets.
For entry-level roles, include relevant courses you took at school
No matter your level of experience, you should always mention the school you attended, what you studied (including any subsidiary subjects or certifications), and when you graduated. This is also the place to list any intensive courses or relevant online courses you may have completed in the field.
If your background is in academia, you can also list any publications you may have co-authored. Be sure to include the title of the journal and a link to allow the hiring manager to read further if they’re interested.
Only mention your degree classification on your CV if it’s something you want to highlight—generally, only list your classification if you’re just starting out and achieved anything above a 2:1.
Key certifications for a data analyst
Certifications demonstrate your expertise by validating your skills, showing commitment to continuous learning, and making your CV stand out. You need that competitive edge, especially in the data science field, where you’ll be up against equally qualified candidates and need to convey your value to employers who know what they’re looking for.
Only include certifications relevant to the role and aligning with industry demands, trends, and tools.
How to list a certification in your CV:
Google Data Analytics Professional Certificate — Coursera, 2024
Top qualifications for a data analyst:
- Microsoft Certified: Power BI Data Analyst Associate
- IBM Data Analyst Professional Certificate
- SAS Certified Advanced Analytics Professional
- Certified Analytics Professional (CAP)
- Tableau Desktop Specialist
- AWS Certified Data Analytics – Speciality
Work experience
You analyse data for a living, so you know that numbers matter when it comes to information. So when you’re talking about your work experience, your goal should be to highlight your achievements using numbers and estimates.
The formula for discussing work experience
Specific contribution to project mentioning specific tools and skills
+
quantitative effect of the project
Example:
Conducted a customer cohort analysis using SQL and Excel and recommended an email campaign for one customer segment.
+
which increased monthly retention by 10%
Enter your text here…
When discussing your work, especially if it was a team project, emphasise your specific contributions. For example, you may have made a product recommendation based on a previous analysis. You’d want to talk about that particular recommendation on your CV instead of the developed feature.
When discussing the quantitative impact, it’s fine to talk about the project as a whole. Following the example above, it would be impossible to separate the value of your product recommendation from the engineer’s contribution who developed the feature, as it’s a team effort. You’d state that the feature had a revenue impact of $X on your CV.
Data analysts work across many different teams and projects in a company, so it’s not always easy to link your work to a revenue impact. Still, try estimating your contributions using metrics to make your CV stand out.
These can be very rough estimates; you just want to make it clear that you’ve contributed to positive outcomes for the businesses where you’ve worked.
Ways to measure the impact of your analytics work
- Improved customer conversion rate
- Used Python and SQL to identify a specific change in the landing page, resulting in a 10% increase in free trial activation rate
- Saved manual reporting time
- Streamlined and automated a key business report in Tableau, saving the team 10 hours of reporting each week
- Lowered costs
- Used SQL and Excel to recommend terminating contracts with the poorest-performing suppliers, resulting in an annual cost saving of £100,000
- Created data visualisations to assist executives
- Created data visualisations in Excel to demonstrate the effectiveness of the marketing plan, resulting in the closure of a £1.3 million Series A funding round.
- Enhanced customer loyalty
- Using analysis in Python, I determined that emailing customers who had been inactive for 7 days led to a retention improvement of 7 basis points.
- Improved business-specific KPI such as time-to-hire
- Identified procedural areas for improvement in recruitment data to reduce the time-to-hire for key roles by 11 days
- Enhanced customer satisfaction
- Used SQL and Excel to identify common complaints among new customers, leading to changes that improved new customer satisfaction by 14%
When formatting your work experience, always list your most recent work at the top of your CV and list your other positions in reverse chronological order.
To further emphasise our point, here’s an example of the same work experience. One is expressed with a quantitative impact, and one is not.
Poor—no measurable impact
Good—quantitative impact
Tailor your CV for each job
For each role to which you apply, make minor edits to your CV based on the data analyst job description. Fortunately, you don’t have to completely rewrite your CV; just a few tweaks will do.
For example, let’s say you’ve completed projects in both Python and R, and your CV heavily emphasises your Python experience. If you apply for a job that mentions R, you should modify your CV to highlight your R experience.
Similarly, if you have specific projects that relate to the job you’re applying for, include those projects. If you’re applying for a marketing data analyst role and have experience building marketing mix models, your application will become significantly stronger by mentioning those mix models.
Let’s say you’re applying for this job:
This seems like a demanding data visualisation role. Instead of mentioning predictive modelling, discuss in detail your experience in creating robust data visualisations in Tableau.
Change this:
To this:
Key points
Here are the major points you should keep in mind when writing a professional CV:
- Keep it to one page and proofread, proofread, proofread.
- For an entry-level role, mention any maths/stats/econ/programming courses you took at university.
- Otherwise, don’t let your education section take up too much space.
- You don’t need a summary or objective section on your CV unless you’re undergoing a career change or have over 10 years of experience.
- Only include skills on your CV for which you’d be comfortable being interviewed.
- Mention your specific contributions and quantify the overall project’s impact on the business.
By following this guide, you’ll be able to quickly and convincingly demonstrate that you’re an excellent fit for the data analyst position for which you’re applying.
Applying for jobs isn’t easy, but you’ve taken a huge first step towards landing that dream job. Now all that’s left is to write, double-check your CV for errors, and submit it to your dream job!
Data Analyst CV FAQs
Begin with a tailored data analyst CV that aligns with the job descriptions. Next, include your key skills, highlight projects, and work experience with measurable outcomes. It’s also important to support your qualifications with certifications that give recruiters a sense of your professionalism and passion for the field.
A strong data analyst resume should include:
✅A concise professional summary
✅Key technical and soft skills
✅Industry-specific certifications
✅Work experience/projects with quantified results
✅Software and tools such as SQL, Python, and Power BI
Education
Yes—more than ever. The U.S. Bureau of Labor Statistics’ projects that data-related roles will grow 35.8% between now and 2031, making it one of the fastest-growing professions. With a well-crafted data analyst resume, you can capitalize on this surge and open doors in finance, healthcare, and tech industries.
For a compelling entry-level data analyst resume, emphasize the following:
✅Projects showcasing the use of multiple data analytics tools
✅Your genuine interest in data
✅A STEM degree
✅Graduating from a high-ranked school
✅Personal projects, such as in machine learning
