What makes for a great data analytics resumé? Is there a specific layout to follow, and what skills should you highlight? Keep reading to find out.
Looking for a job as a data analyst? Exciting times! This fast-growing industry offers tonnes of career development opportunities—and it can pay pretty well, too. Of course, with all this and more going for it, the competition can be pretty stiff. So, as an aspiring (or job-seeking) data analyst, it’s essential to get your resumé right. To improve your chances of a job interview, your resumé should stand out while ticking all the requirements outlined in the job description.
Whether you’re new to data analytics or looking for your next challenge, this post covers everything you need to know to create a winning data analytics resumé. To make things as easy as possible, we’ll use plenty of examples to illustrate the best approach.
We’ll cover:
- What should you include in your data analyst resumé?
- A note on your name and contact details
- How to write a good introductory paragraph
- Top hard skills and tools for your data analytics resumé
- Main soft skills to highlight in your data analytics resumé
- Work experience and qualifications
- Listing other achievements and activities
- What’s the difference between entry-level data analyst resumés and senior data analyst resumés?
- Your final data analytics resumé checklist
- Summary
So: How do you write a great data analyst resumé? Let’s take a look.
1. What should you include in your data analyst resumé?
Data analytics jobs can cover a wide range of industries, from sports to healthcare, marketing, the sciences, and more—you can learn more about where a career in data analytics could take you in this guide. Despite this, data analytics resumés all ultimately serve the same purpose: To help hiring managers select who to invite for an interview.
A common statistic is that recruiters only spend 7 seconds looking at each resumé. Whether or not this estimate is accurate, one thing’s for sure: Recruiters are busy people. So make their job easier by following a standard resumé format.
Data analytics resumés (like any other) should be no more than one page. If yours is longer, you should aim to cut it down (there are exceptions, but we cover these in section eight). In general, though, you should include the following on your data analytics resumé:
- Name and contact details
- Introductory paragraph
- Tools, languages, and skills (this includes hard and soft skills)
- Work experience and qualifications
- Additional achievements and activities (optional)
In the following sections, we explore these in more detail, with examples.
Should you include a photograph and your date of birth on your resumé?
You want to stand out, right? What better way to do so than with a nice photograph of yourself? Wrong! If you’re tempted to include a headshot, check the local employment laws for your country or region first. Providing photos (and in some cases dates of birth) can breach equality guidelines. This means that including a picture may automatically disqualify you. This varies on a regional basis, though, so be sure to check before you hit send. However, if in doubt, don’t. With that covered, let’s get going…
2. Your data analyst resumé: Name and contact details
It might sound obvious, but when it comes to your name and contact details, keep things punchy. When you have a one page resumé, every line counts. Data analysts need to demonstrate excellent visual and communication skills, too, and this should be clear from the very start.
The only contact details you need to include on your resumé are your name, email address, and phone number. Avoid nicknames and don’t use your work email address (or that one you created in high school that you haven’t got round to changing yet!)
Good example:
Joanna Larkin – 202-555-0126 – J.Larkin@example.com
Bad example:
Jo-Jo Larkin – 202-555-0126 – JoEatsDonuts@example.com
You can also include your postal address if you like, although this isn’t strictly necessary. If you want to show that you live in the city where the job is located, it can be handy to include it, but use your judgment. It can also be a nice touch to provide links to the following:
- Website or portfolio. We show you how to build a data analytics portfolio in this guide.
- LinkedIn profile
- GitHub account
- Other social media profiles
However, only provide links to relevant projects or information. For instance, don’t bother linking to your GitHub if you haven’t uploaded any projects, as this won’t look great. Likewise, make sure your LinkedIn profile is up to date, including any recent, relevant expertise.
Including social media or your blog can also be a great way to showcase your interest in industry trends. For example, your recent social media activity might demonstrate that you take an active interest in the latest data analytics or machine learning developments. Be aware, even if you don’t list your social media accounts, potential employers might search for them. So do a quick mine sweep for anything you wouldn’t want potential employers to see… We’re talking political views, embarrassing photos, or silly dancing videos (we’ve all got them!).
3. How to write a good introductory paragraph for your data analyst resumé
Next, and perhaps the most important part of your data analytics resumé, is your introductory paragraph. As mentioned, hiring managers are busy people, and the introduction is the first (and often only) part of your resumé that they’ll read. Think of it as your hook. Get it right and they’ll read on. Get it wrong, and it doesn’t matter how great the rest of your resumé is—you’ll end up in the “no” pile.
If you’re an experienced data analyst, you can title this section “summary” (i.e. of your experience). If you’re newly qualified, title it “objectives” or “goals” (i.e. where you want to go with your data analytics career). Either way, it should be direct, fact-based, enthusiastic, and tailored to suit the job. While this means your introduction will be different for every data analytics resumé you send out, this will make all the difference. Let’s take a look at how each option might read:
Summary of experience (for more experienced data analysts)
Good example:
Process-driven data analyst with 2+ years’ experience analyzing business data at InfoCorp. Proven track record of boosting marketing leads, leading to a 20% increase in revenue. Special skills lie in predictive analytics and data visualization using Tableau. Keen to build on these skills in an exciting new role.
Bad example:
Two years working in data analytics. Bored in my current role so looking for new opportunities. I’ve got all the essential skills of a data analyst so let’s talk face to face.
The first example uses active, positive language while highlighting specific skills and experience, e.g. Tableau. It also includes measurable, numerical achievements (i.e. the 20% increase in revenue). The second example is lackluster, negative, passive sounding, and vague. It’s also a little arrogant—don’t simply tell the employer that you meet their requirements. Explain how, with brief examples.
Objectives / Goals (for entry-level data analysts)
Good example:
Graduated from John Collins University with a degree in Business Management. Spent three years leading change projects at KPMG. Fascinated by the impact of data on business operations, I retrained as a data analyst. Hoping to blend my newfound data analysis skills with existing business knowledge to bring unique insights to this role.
Bad example:
Graduated with a degree in Business Management and am now looking for a career change, so retrained as a data analyst. Open to any entry-level job that requires data skills.
If you haven’t worked as a data analyst before, the main takeaway here is to be positive and to frame your transferable skills and enthusiasm as key reasons for hiring you. Whether your past career was in an office, working in retail, or anything in between, focus on drawing out your transferable skills.
For entry-level roles, good companies will understand that your skills are limited. They will not necessarily expect you to know how to conduct complicated analyses or create complex machine learning algorithms. Ultimately, the hard skills are something you can learn. A good attitude, meanwhile, is rarer to find.
4. Top hard skills and tools for your data analytics resumé
Hard skills (or learned abilities) are vital for any role. They’re especially important for a technical field like data analytics, where you’ll need certain prerequisite skills to do the job. It’s vital, therefore, to list the right hard skills on your data analytics resumé. This is not only for human eyes. Many companies use automated applicant tracking systems, which search for the correct keywords and filter out resumés that don’t fit the requirements. This makes it doubly important to list the appropriate hard skills on your resumé.
Within the field of data analytics, hard skills can be broadly divided into two categories. These are software (relevant tools and programs) and learned skills (data-specific knowledge, such as how to conduct a regression analysis). While you don’t necessarily need to separate these on your resumé, keep them in mind—this will help you stay focused on the story you’re trying to tell.
Hard skills to include on your data analytics resumé
Always start by looking at the job description. This will contain the key hard skills that the hiring company needs. They’re often separated into “essential skills” and “desirable skills”. Make sure you can tick off all the essential skills on the list and include as many of the desirable ones as possible. Include added extras, if appropriate. For instance, if you have a particular interest in, say, prescriptive analytics, or random forest algorithms, it can’t hurt to mention it—even if it’s not explicitly required by the job description.
Example 1: Data analytics hard skills
One way to make good use of limited space is to align your skills to the overarching steps of the data analysis process. The following is an example of common skills you might need for an entry-level position, and how you might list them:
- Research – Data mining, survey creation, focus group management
- Data management – Database design, SQL, pattern identification, data cleaning (e.g. pandas)
- Statistical analysis – Exploratory data analysis, prescriptive and predictive analysis
- Computer science – Advanced MS Excel,Python, R, machine learning algorithms
- Visualization – Using tools such as Tableau, Knime, MS Power BI, Matplotlib
- Presentation skills – MS PowerPoint, Jupyter Notebook, R-Notebook
Example 2: Data analytics hard skills
If you’re new to data analytics, you can also use ‘skill bars’ to highlight your level of expertise. Are you a beginner, intermediary, or expert? If you’re a whizz with visualizations or using Adobe InDesign, you can find a nice graphical way to show this (bonus—this will also show off your visualization skills!) Alternatively, you can simply do so using a word processor:
- Python: Expert
- MS Excel: Expert
- Tableau: Intermediate
- JavaScript: Intermediate
- R: Beginner
This might seem oversimplified, but it’s very helpful for a hiring manager to see a quick list of your hard skills. However, do mention your key skills in more than one place, if possible. For instance, you can incorporate them into your introduction, or in the work experience section of your resumé. This will increase the chances of a busy hiring manager spotting your hard data analytics skills, as well as helping your resumé get through that all-important applicant tracking system!
5. Main soft skills to highlight in your data analytics resumé
It’s easy to assume that hard skills are the only important thing for data analytics jobs. Indeed, they’re highly valuable. However, you shouldn’t overlook your soft skills. These include things like openness to feedback, the ability to communicate well with different people, and work ethic. Combining soft and hard skills will go a long way to helping you secure an interview.
Important soft skills for a data analyst resumé include:
- Communication and public speaking
- Strong report writing skills
- Storytelling abilities
- Business sense
- Critical thinking, i.e. the ability to think skeptically
- Team working (it’s a classic, but it’s still important.)
- Time management
- Adaptability and creativity
- Risk awareness
When you’re pressed for space, it might seem a nuisance to include many of these. Rather than simply listing them (as with hard skills), try weaving them throughout your introductory statement, work experience, or additional achievements section. Use examples where possible. This might sound tricky, but it’s actually a good thing, especially if you’re new to data analytics. That’s because many of these abilities are transferable and you don’t need to be an expert data analyst to have them. You could just as easily have picked them up while studying at university or working at an ice cream parlor.
6. Work experience and qualifications
After your introduction, list your work experience. This will highlight your skills and interests in action. Always list your work experience in reverse chronological order, putting your most recent job first. List the job title, name of the organization, and dates you worked there. Then include a bulleted list of your key tasks and responsibilities (two or three bullets will usually do, unless it was a senior role). If you’ve only worked as a freelancer, you can title this section “projects” and pick a few of your most interesting ones.
If you’ve worked in two or three relevant positions within data analytics, don’t feel compelled to include an exhaustive list of your entire work history. Just the most impressive roles will do.
Meanwhile, if you have a limited number of past roles, use other experience to highlight your transferable skills. You could also mention any important data portfolio projects you’ve worked on. Below, you can see how this might look (and how it definitely shouldn’t!):
Good example:
June 2018—Present
Financial Data Analyst
InfoCorp
- Helped boost marketing leads by 22%
- Created data visualizations using Tableau
- Generated monthly reports for senior management using pandas
May 2017—June 2018
Retail Manager
Lucky Scoop Ice Cream Parlor
- Managed a team of five, including quarterly appraisals
- Proposed solutions for improving customer satisfaction and reducing expenses
Bad example:
InfoCorp
- Analyzed data using data analysis tools
- Reported to senior management
Lucky Scoop Ice Cream Parlor
- Told less senior staff what to do
- Gave refunds to complaining customers
Past experience and projects needn’t take up lots of space, but they should include key skills and examples. Consider the position of the hiring manager. What can you tell them about yourself that they don’t already know? What will compel them to pick up the phone and ask you for an interview?
After listing past projects and work experience, you should include your qualifications. Just like your work experience, these should be in reverse chronological order. Make sure you include your degree (if you have one) and relevant data analytics certifications. If you’ve completed a data certification program or a data analytics bootcamp, this will certainly impress employers, so be sure to feature it prominently on your resumé.
7. Other achievements and activities
If you have space, it’s nice to include a section that highlights your other achievements and activities (i.e. those that lie outside work or academic experience). Especially for entry-level data analytics roles, this is a good way to highlight your suitability. In the past, people used “hobbies and interests” sections to highlight extra-curricular activities. This is a bit outdated now, but an achievements section runs with this idea by emphasizing things that showcase your abilities. For instance:
- Leadership skills: Perhaps you run or participate in a club in your spare time, e.g. sports groups or events?
- Relevant interests: Have you contributed to an industry publication? Do you have a blog where you publish on relevant topics, e.g. machine learning or artificial intelligence?
- Domain expertise: If you’re applying for a job as a sports analyst (for instance) why not mention that marathon you ran last year? Did you wear a Fitbit? What insights did you obtain?
- Awards: Have you won any awards for your work? This could be as simple as “employee of the month”, a business award, or maybe even a Kaggle challenge?
If you’re including an achievements section, be clever with it. Only list your hobbies if they’re relevant. For example, “going out with friends” won’t tell an employer anything very useful about you, whereas being a regular attendee of a data analytics meetup will. Stay on topic and make sure the items you include sell the best of you. Don’t worry if your data analytics resumé doesn’t include everything. Highlight the most compelling things and you can save the rest for the interview.
8. Entry-level data analyst vs. senior data analyst resumés
Regardless of the job you’re applying for, the overall layout of your resumé should follow the outline we’ve described above. However, if you’re applying for a senior data analyst role, there are a few differences and additions to be aware of.
Summary / Introductory paragraph
Senior data analyst resumés won’t get away with any vague wording in the introductory paragraph. Instead, offer a clear idea of your leadership skills, using very specific examples. For instance, you might mention teams you’ve managed, the projects you’ve overseen, and their ultimate outcomes. Always use measurable figures or percentages where possible, such as improved customer retention figures or other key performance indicators (KPIs).
Qualifications
While work experience usually comes before qualifications on any resumé, if you’ve spent the past seven years doing a Ph.D. in mathematical computing (for example) it might be more relevant to put this first. Meanwhile, if you have any other qualifications or letters after your name, include these at the top, or use a designated heading to showcase them.
Hard skills
It might seem obvious, but as a senior analyst, your skills section should be more nuanced to reflect the more demanding requirements of a higher-level role. If you’re applying for a more senior data science position (rather than something entry-level) the hiring manager will want to see information about your specific domain expertise. This might include things like engineering, finance, psychological profiling, or other STEM subjects. It should also mention your advanced skills in areas like artificial intelligence, natural language processing, data infrastructures, or algorithms you’ve created.
Affiliations, groups, and publications
Senior data analyst resumés also need a section that lists volunteer positions, board memberships, or professional affiliations (such as memberships of industry bodies like the Digital Analytics Association, or the Open Data Institute). You should also list any research papers or other publications you might have worked on.
Overall length
For all the reasons above, senior data analyst resumés can break the one-page rule. This is because you’ll need more space to highlight your additional relevant expertise. If possible though, still aim to keep your resumé to two sides. You can always direct employers to your website for more information.
9. Your data analytics resumé: The final checklist
We’ve come this far, so let’s not fall at the last hurdle! Silly mistakes can be the death-knell of any job application. Once you’ve completed your resumé, use the following checklist to make sure it’s as polished as it can be.
Have you researched the company?
Before submitting any resumé, always research the company you’re applying to. For instance, a resumé for a sales analyst role is likely to be quite different from that of a healthcare analyst. Make sure you get a sense of the company culture, what they do, and the language they use. Frame your data analytics expertise to match.
Have you included all the relevant keywords?
We’ve mentioned this before, but it doesn’t hurt to drive the message home—check that you’ve included the relevant keywords, both for the hiring manager and those pesky applicant tracking systems. Not all companies use them, but if you’re applying for a job online, it’s a real possibility. Better to err on the side of caution.
Have you looked at your data analytics resumé with a fresh eye?
Printing your resumé—or even just changing the font on-screen—is a great way to spot any missing information, formatting errors (e.g. inconsistent headings or bullet points), and for giving it a general sense check. If you can, sleep on it. You’ll be surprised what you’ll spot with a fresh eye. If possible, get someone else to check it, too. They may catch mistakes you’ve missed or suggest additional skills and experience that you should include.
Have you backed up your achievements?
When making grand claims, be sure to back them up. If you’ve said that you specialize in machine learning, prove it—include some examples of your work. Quantifying your achievements will impress a potential employer much more than simply telling them that you’re qualified.
Have you spell checked?
Often, applications don’t progress simply because someone has used poor spelling or grammar. An eye for detail and clear communication is vital for data analytics jobs, and your application should reflect this. Don’t just rely on the automated spellchecker, either. These don’t always pick up the nuances of language and won’t catch everything. For instance, you definitely don’t want to get the company’s name wrong!
Does your data analytics resumé fit on one page?
Too long? It’s OK to get a little creative with columns and bullets if that helps you get everything on one page. It’s also fine to write in note form, as long as what you’re writing makes sense. You can always include additional information on your website or portfolio. Remember: You don’t need to tell employers everything, just enough to whet their appetite for more.
Save creativity for your portfolio
Creativity is great, and it’s a highly sought after skill for data analysts. However, when it comes to your resumé, don’t go too wild. Aim for clarity on your resumé. Use a clear, standard 12-point font and save the real creativity for your portfolio. And, if you need some inspiration for your data portfolio, here are nine of the best data analytics portfolios on the web right now.
10. Summary
In this post, we’ve covered the key things you need to think about when you’re writing your data analytics resumé. To recap:
- Follow a standard format: At a minimum, include your name, contact details, an introductory paragraph, a list of key hard and soft skills, work experience, and qualifications.
- Include additional achievements and activities if you can, but only list things that are relevant to the role.
- Don’t rush your introductory paragraph—it may be the only part of your resumé that an employer looks at, so it needs to make an impact.
- Include essential hard skills: Data analytics jobs require very specific technical expertise, so it’s vital to include everything listed in the job description, from your Python skills to your knowledge of statistical analysis. Big yourself up, but don’t embellish.
- Weave both your hard and soft skills throughout each section and try to mention the important ones in several places.
- Keep in mind what the hiring manager is looking for. This will help you stay focused and decide which information to include (and what to leave out).
- Keep it short: For entry-level jobs, your data analytics resumé shouldn’t exceed one page, but for more senior roles, you can stretch to two.
If you’re new to data analytics and want to find out more, why not try our free, five-day data analytics short course? Meanwhile, for more tips and advice on forging a career as a data analyst, check out the following: