The number of data analysts is expected to grow by 25 percent between 2020 to 2030, coupled with the increase in pay transparency laws making this the ideal time to get a data analyst job.
Fun fact: before starting BeamJobs, one of our founders worked as a data analyst for six years. With his guidance, we’ve reviewed many data analyst resumes to figure out what helps data analysts get more interviews.
Building a resume and data analyst cover letter is the hardest part of this process. To inspire you, we chose 29 top data analyst resume samples for different career stages.
Our data analyst resume examples are proven to help you put your best foot forward to get the job you’ve always wanted in 2024.
According to the U.S. Bureau of Labor Statistics, the employment of computer and information research scientists (including data analysts) is projected to grow 16 percent from 2018 to 2028. This is much faster than the average for other jobs!
Due to the high demand and high wages, it makes sense that people are flocking to apply for data analyst roles.
But that doesn’t mean you should be discouraged. Applying online to jobs can feel like applying in a black hole, and we know it sucks. It took one of our founders 77 job applications before he landed his first data analyst job at Geico, and the next job was much easier to get.
One issue with applying for data analytics roles is that these titles are not standardized across different companies. At one company, data analysts might spend their time building reports in Tableau, while at another, they might be writing machine learning models for production.
Because of this variability, it can be tough to be sure to include the correct information in your data analytics resume. With this guide, you’ll put your best foot forward, no matter which data analyst roles you’re seeking!
First, you need to show you have the right abilities for the job! This means you need to accomplish two goals with the skills section of your data analyst resume. First, you have to be able to get past the automatic keyword filters in the applicant tracking system (ATS), which companies use to filter applicants. Next, you want to demonstrate your technical proficiency to the person reviewing your resume.
If you’re unsure which skills to include, we analyzed the data to learn which skills are most in demand for companies hiring data analysts.
These two goals are, unfortunately, mostly in opposition to each other. If your goal was to just get past the ATS, you’d list every skill to get your foot in the door:
Bad—avoid a long list of generic skills
The problem? This method is a big red flag 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 take up more than 20 percent 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 coded in before. And avoid including a skill like “data mining” or “data analysis” since a technical hiring manager knows these are just blanket 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 hard to read. Never fear; there are multiple ways to organize your skills section! For starters, you can list your skills by how proficient you are with them (“Advanced” vs. “Familiar,” for example). Alternatively, you can list your skills by skill 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 visualization and manipulation in that programming language.
Good—specific skills and modeling techniques
Most of the time, you don’t need to include a resume objective or resume summary; a mistake many data analysts make. If a resume summary or objective doesn’t add value to your application, it’s okay to leave it out.
Here’s a sample data analyst resume objective that you would want to leave off of your resume:
Bad—uninformative resume objective
Why leave this off? It’s redundant. Suppose you already demonstrate in your resume that you used tools like Python and SQL to turn data into actionable insights. In that case, your objective doesn’t tell the person reviewing your resume any new information.
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 resume summary or an objective; all you need to know is when you should include them.
Here are some quick tips for formatting your data analytics resume:
When a hiring manager reviews 50+ resumes for a given role, they quickly look for reasons to say “no.” By using these resume-formatting tips, you make it easier for the hiring manager to see your worth and ask you for an interview, getting you one step closer to a job.
Of all the places to make an error, your contact information is the worst place to have it happen. One of our team members recounted their early days out of college as a data analyst. When they were applying for jobs, they accidentally wrote the wrong email address on their resume 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 role you’re applying for (even if it’s not your current role).
You don’t need to include your full address in this section, but you should list your city and zip code. 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 convey why you’re a great data analyst. If you have an active Github, include a link to that. If you do a lot of Kaggle contests, include a link to your profile. Have a personal blog where you talk about election data? Be sure to include a link.
If you’re entry-level and looking for your first full-time role, including projects on your data analyst resume is an absolute must. However, the more work experience you get, the more projects should become less critical. By the time you have four-plus 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 an answer to that problem. It’s okay if it’s a class 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 analyze data to sate 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, analyzed 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 ground-breaking. Employers just want to see that you can ask a question, use data to answer it, and present your findings reasonably and clearly.
Good—show you can answer your own questions with data
When talking about your projects, here’s how you should frame what you did:
Your projects section is also an opportunity to provide more context around the programming languages and libraries you listed in your “skills” section.
Like the “projects” section, the education section of your resume will be longer for entry-level data analysts relative to more experienced data analysts. You’ll want to include relevant courses you took in school related to data analytics for entry-level data analysts.
Courses relevant to data analytics are any mathematics, statistics, programming, and economics classes you took. To be an effective data analyst, you need to apply the principles you learned in these classes to real-world problems and datasets.
For entry-level roles, include relevant classes you took in school
Regardless of your experience level, you should always mention the school you attended, what you majored in (including minors or certifications), and when you graduated. This would also be the place to list any boot camps or relevant online courses you may have taken 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 magazine and a link to allow the hiring manager to read further if they’re interested.
Only mention your GPA on your resume if it’s something you want to highlight—generally, only list your GPA if you’re entry-level and obtained anything above a 3.0.
You analyze data for a living, so you know that numbers count when it comes to information. So when you’re talking about your work experience, your goal should be to highlight your accomplishments using numbers and estimates.
“Specific contribution to project mentioning specific tools and skills”
“quantitative impact of the project”
Example:
“Performed a customer cohort analysis using SQL and Excel and recommended an email campaign for one customer segment”
“that lifted monthly retention by 10%”
Enter your text here…
When discussing your work, especially if it was a team project, emphasize 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 resume instead of the built feature.
When talking about the quantitative impact, it’s okay to talk about the project as a whole. Following the example above, it’d be impossible to tease out the value of your product recommendation versus the engineer’s impact who built the feature since it’s a team effort. You’d say the feature had a revenue impact of $X on your resume.
Data analysts work across many different teams and projects in a company, so it’s not always easy to tie your work to a revenue impact. Still, try estimating your contributions using metrics to make your resume 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 worked.
When formatting your work experience, always list your most recent work at the top of your resume and list your other positions in reverse-chronological order.
Just to hammer home our point even further, here’s an example of the same work experience. One is stated in a quantitative impact, and one is not.
Bad—no quantitative impact
Good—quantitative impact
For each role to which you apply, make minor edits to your resume based on the data analyst job description. Fortunately, you don’t have to completely rewrite your resume; just a few tweaks will do.
For example, let’s say you’ve done projects in both Python and R, and your resume heavily leans into your Python experience. If you apply to a job that mentions R, you should change your resume to discuss 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 to this job:
This seems like a heavy data visualization role. Instead of mentioning predictive modeling, talk extensively about your experience building robust data visualization in Tableau.
Change this:
To this:
Here are the major takeaways you should keep in mind when writing a professional resume:
By following this guide, you’ll be able to quickly and convincingly make the case that you’re a great fit for the data analyst role for which you’re applying.
Applying for jobs isn’t easy, but you’ve taken a huge first step toward landing that dream job. Now all that’s left is to write, double-check your resume for errors, and submit it to your dream job!
