How to Create a Potent Data Analyst Resume

How to Create a Potent Data Analyst Resume was originally published on Springboard.

Just the other day, I received truly dated, awful advice about resume design from a review service. With renewed fervor, I’m committed to helping people who want to create an outstanding resume, not one that will wind up at the bottom of a recycle bin, virtual or physical. This article will make sure you skirt the bad advice landmines as you put together your own data analyst resume. It will arm you with the know-how to write and design an analyst resume that will help you land interviews. Let’s roll.

 

Back to Basics

What do we know about resumes in general? What’s the goal, and why do we have them?

Dictionary time. Merriam-Webster lists three definitions for the noun résumé, which comes from French (hence the accent aigu).

1. Summary
2. Curriculum vitae
3. A set of accomplishments

Let’s start with that first one: summary. Resume derives from the French word for summary. That’s simply one word, and that speaks volumes about resumes and resume design: simple is good. Alright, so what’s a summary, Merriam?

1. Comprehensive, covering the main points succinctly
2. Done without delay or formality, quickly executed

So what do we know about resumes so far?

A resume is a summary, CV, and/or a list of achievements. That summary is succinct, quick, and comprehensive. When we say succinct, we mean that a resume doesn’t include unnecessary details. When we say quick, a more fitting word might be short because a resume gets straight to the point before attention is lost. Finally, when we say comprehensive, we mean that the resume covers everything important.

OK, we know what a resume is now, but we haven’t addressed what it’s for or why we have them.

Leonardo da Vinci is credited with writing the first resume over 500 years ago. The purpose of that first resume, which was really just what people today call a cover letter, was much the same as today’s resumes: to make a case for his qualifications for a particular role.

In other words, while history has changed how people deliver resumes, it hasn’t really changed why they do it. Keep that in mind.

 

What Distinguishes the Data Analyst Resume?

“Without question, the candidate’s need and ability to simplify complex and extremely technical concepts into plain English.” 

That’s just part of the data analyst’s role, according to Sara Livingston, head of operations at Narrative I/O, and that’s really what should distinguish a data analyst resume from another resume.

So what else is involved in a data analyst role?

1. A lot of data collection, sometimes of raw data that needs to be sanitized with scripts.
2. Using technical tools such as Excel to analyze data sets.
3. Communicating data insights, especially visually, to team members.

Related: Data Analyst Job Description

Something to keep in mind: there’s a distinction between a data analyst and a data scientist, but sometimes the roles overlap, or the title of the job might be “data scientist” even though they’re looking for an analyst and vice versa. This means it’s important for you to be able to read these descriptions and identify how you fit.

Related: Data Analyst vs. Data Scientist

 

9 Steps to Write a Data Analyst Resume

Much like the data science resume, piecing together a data analyst resume (which is important for both full-time and freelance data analysis work) can be broken down into steps.

1. Introspect

Just as it’s important to know the goal of the resume, it’s important to know your own goals. Presumably, you want to be a data analyst, but do you want to do it at a large company, or a small one? What sort of experience do you have? A lot? A little? None? All of these are important questions to answer, and have a huge impact on what your resume will look like.

There’s a big difference between expectations for junior data analysts just entering the field compared to those who’ve been in the industry for a while, said Briana Brownell, founder and CEO of Pure Strategy Inc., who frequently writes about hiring data analysts and data scientists.

For entry-level roles, Brownell wants to see “examples of problems that they have tackled,” such as capstone projects.

“Even if it’s not directly related to the work they’ll be doing, it allows me to understand how the candidate thinks about and solves problems,” said Brownell.

Senior data analysts have more to prove, such as how much they have impacted a business, how good they are at “getting buy-in from other parts of the organization,” and their level of technical skill.

Have an idea of where you stand before you get to work.

Related: Which Industry Pays the Highest Data Analyst Salary?

2. Research

You need to know who you’re writing to, how to reach them, and what they’re looking for.

Usually this research is prompted by a job posting, but not always. Cold pitches are still a thing, even if they’re less common. The most infamous, unconventional example might be Matthew Epstein’s pitch to Google, but simply crafting an email with a PDF attached can open doors.

Whether you’re cold-pitching or responding to a job ad, you have to know your audience. A few means of getting this information:

• Check out their website. Look for an “About Us” page. Sift through their blog. Figure out the who, what, when, where, why, and how from the horse’s mouth.
• Get in touch with a recruiter and ask questions, if the door’s open. Be sure you’re prepared if you do this; you might find yourself in the midst of a screening process with your pants at your ankles, and you don’t want that.
• Check out external resources like Glassdoor or LinkedIn.

You might want to write facts down, and while you’re at it, start preparing questions for an interview before you’ve even applied, while the research is fresh.

If you’re not writing to a particular company, use a search engine to compile research on the types of companies you want to work for then synthesize all the job descriptions to get a thorough understanding of what companies are generally looking for. Remember, a generic resume should be tailored for a specific company if you’re responding to their job ad.

3. Organize the Content

For a data analyst resume, as with any resume, there are many options for sections to include. Here’s a breakdown from 30 data analyst resume examples I looked at online.

From this, we glean that there are four sections that always appear: name, contact information, experience, and education. These will be the core of the resume, as they are with almost any resume, meaning they have a high priority. Two sections appear pretty consistently: skills and summary. Everything else depends on whether space is available or it’s relevant.

What order should these go in? It depends. This is why we’ve done research. Put the name at the top, or make sure it’s prominent. Everything else is a moveable part. Just ensure the core sections take up the majority of the resume’s space.

An example of how the resume might be organized would be:

1. Name
2. Contact information
3. Experience
4. Skills
5. Education

I’ve chosen to omit a summary because, honestly, it doesn’t serve much purpose. Think about it this way: what’s the definition of a resume again? It’s a summary. Do we need a summary of our summary? Probably not.

4. Write the Content

Here’s the most important part of the resume: the content. I encourage you to write it out in plain text using a program such as Notepad or WordPad. A couple of reasons for this:

1. Larger companies that use an applicant tracking system (ATS) want something they can easily parse. Plain text ensures less is lost in translation.
2. You focus more closely on the content without being distracted by visual concerns.

Once you’ve written it this way, save the document for potential future use with an ATS, as well as a place to edit the content later. Don’t worry about length for now. Worry about that for the laid-out version.

Some tips for the writing process:

• Clearly label sections with headings.
• Write in present tense. These verbs pack more punch.
• Favor active voice. Learn more here.
• Emphasize results over action. For example: “Conduct data mining and modeling in coordination with finance department.” Try to word this to show the typical result from this day-to-day interaction, something like: “Produce actionable business information through data mining and modeling in coordination with finance department.” The goal here is to show not only that you perform the duties of your role, but you think about and understand how your role fits into the bigger picture.
• Use as few words as possible. Space is a serious consideration here. Here’s a good resource on concise writing.
• Cut phrasal verbs.
• Edit. Then edit some more. Send it to someone you trust to edit. Then send it to someone else. There’s science behind our inability to catch typos.
• Tailor your content for the position for which you’re applying. Use keywords from the job description. Present all the relevant information in the best possible order (most relevant at top).
  

5. Brainstorm Format and Layout

This article focuses primarily on a standard resume output to PDF or .doc, but resumes come in many different forms, from videos to printouts. If you’ve done your research, you’ll know what format the company wants. If the resume isn’t for a particular company, just make sure you’re covering your bases with a Word doc, at the very least. For our purposes, this is going to be single, letter-size page.

With that in mind, we can start laying the resume out. I always start with dummy sheets.

As you can see, these don’t need to look pretty. (Excuse the atrocious handwriting.) What’s important is that you identify key sections and how they fit within the overall composition of the page. Center-aligned, single-column layouts are some of the most common resumes even today, though they’re rather bland, inflexible designs that waste a lot of space. Some employers actually prefer them. It’s OK to center headings, but avoid doing so with all content. Left-aligned, single column layouts waste a little less space, but they’re not much better (just cleaner).

What I find works well in terms of spatial efficiency is to use some form of columns. This allows you to mix center-aligned elements (which tend to stick out a bit more) with more readable left-aligned sections. Personally, I usually employ the two-column, left-aligned layout. It’s not the most interesting, but it’s flexible and gets the job done. Centering the name has the effect of making it the focal point. Beyond experience, the name is the most important element.

6. Design the Layout and Work in the Content

Most recruiters will tell you that your design doesn’t factor heavily into whether they extend an interview opportunity.

“It’s mainly about conveying the content in an easily comprehensible way,” said Livingston.

“The resume just needs to be functional: clear delivery of pertinent information,” said Dary Merckens, chief technology officer for gunnertech.com

Functional is an excellent word to keep in mind. What many recruiters mean when they say design doesn’t factor heavily into the decision-making process is that your resume doesn’t have to look fancy. And it doesn’t. If you’ve ever taken a graphic design course, you know that proper design is about communication. Not all designs are sexy. The best designs are functional. Good design is silent, and that’s what you want to look for in your resume design.

So how do you make your resume design functional?

• Create separate sections. This will make it easier for the recruiter to identify important relevant information quickly.
• Use bullet points for lists. Just do it. They make everything neat, clearly separating items visually so reviewers can go through content quickly. Remember, summaries are quick.
• Never put something on the page just because it looks pretty.
• Make headings at least twice as large, and as bold, as body type.
• Align items for legibility, meaning: prefer left alignment.
• Keep related elements grouped (headings and their content). Add more space between separate sections.

Have someone you trust look over your work, ideally a professional writer and/or designer. Everyone needs an editor. Just remember: if the design has suddenly become the focal point of your resume, you’re doing it wrong.

7. Get It Online

This step is optional, but highly recommended. Many venues allow you to publish a resume online: LinkedIn, Glassdoor, ZipRecruiter, Indeed, and much more. Creatives usually have their own website. It wouldn’t hurt to have one as a data analyst either. I’ve often had recruiters contact me after finding my resume on Indeed. A carefully crafted online presence can offer major advantages. Just make sure the information has cross-platform consistency.

“A candidate should always assume going in that the interviewer has already checked out their LinkedIn profile, which makes it incredibly important for the candidate to ensure there’s consistency across their resume and LinkedIn profiles,” Livingston said.

Related: 25 Websites to Find Data Science Jobs

8. Send It Out

Send the resume out with the application for the job to which you’ve specifically tailored it. If possible, get a resume in front of human eyes. This is usually possible with startups and small companies. Other times, there’s a gatekeeper in the form of an ATS. If it’s a Fortune 500 company, this is probably the case. That’s where you might want to dust off your plain text resume.

As a note about ATS, tailoring is particularly important when submitting to a system that filters resume based on fit. Human beings can look at a resume and say, “Well, this person doesn’t exactly match the criteria, but it balances out because they have X amount of experience. Plus, they seem rad.” An ATS doesn’t really do that, so make sure the keywords from the job description are present and that dates for employment add up to the amount of experience required.

9. Bring It to the Interview

If you landed an interview, this article has done its job. Just print out a few copies of the resume to bring along to the interview. This shows thoughtfulness and ensures they get the authentic version, rather than an HR printout.

 

Conclusion

The data analyst resume is a foot-in-the-door tool, as was da Vinci’s letter over 500 years ago. While looks can help with this, they’re not the focus. The focus is to provide a summary, using the steps above, of your qualifications for a data analyst job. There’s not much more to it. More than writing the resume, the goal throughout your career should be on expanding your qualifications, and, of course, enjoying your job.

To further understand what you need to know to launch your data science career, check out Springboard’s guide: How to Get Your First Job in Data Science.

The post How to Create a Potent Data Analyst Resume appeared first on Springboard Blog.