While everyone understands that resumes are crucial to their career success, many job seekers lose sight of the main objective of their resume. Simply put the only, and I mean only, purpose of a resume is to pique the interest of the hiring manager or recruiter enough to bring you in for an interview. Full stop. To do that you need to give potential employers some reason to think you can actually do the work they need done.
So how do you interest them enough to actually call you back? Well the first step is to make sure you don’t give them a reason to throw your resume out. Even though data analysts are in high demand, employers are not going to lower their standards just to make a hire.
In my previous management roles I’d often get 100+ job applications for each of my job openings. As I read through the resumes I’d often discard dozens for every one I moved forward for a phone screening. I read each resume for 30-60 seconds before deciding whether the resume deserved a more thorough read through. If I saw a couple of red flags I’d simply toss the resume and move on to the next one.
To help increase the chances that your resume gets read and not tossed I’m going to give you 10 common mistakes to avoid. While they don’t guarantee a call back they at least prevent you from losing out on the opportunity for a reason within your control.
#1 – Writing About Responsibilities Instead Of Achievements
Often when we write our resume, we are so focused on ensuring the reader can understand our previous work experience that we write about responsibilities and forget to highlight our achievements. The result is a list of bullet points that read more like a job description than actual qualifications.
While it is important that the person reading your resume can mostly understand what you actually did, listing the work alone is almost certainly not enough to get you an interview. You need to demonstrate how you went above and beyond in the role and achieved tangible results that had a business impact.
Let’s look at a simple example. Many first-time data analysts support their team’s recurring reporting. Below are two ways to describe the same work. The first just says that the analyst had to support a certain number of reports and that they had to ensure they went on time and without issues. The second bullet says the same thing but gives metrics to demonstrate tangible results that any leader would be happy with.
Maintained over 30 monthly reports and ensured they were delivered to users on time and accurately.
Maintained over 30 monthly reports that were used by over 100 users. Consistently had timeliness and accuracy rates of over 98% and made enhancements to processes and underlying data sets which resulted in 20 hours of saved time monthly.
While it might be difficult to get an achievement on every bullet in your experience section, try to make sure at least 50% of them have some sort of measurable results.
#2 – Talking About Collective Contributions Instead Of Your Own
Another common mistake I see on data analyst resumes is the tendency to focus on what your team accomplished instead of your own contributions. This mistake is understandable as many places reward you for focusing on teamwork and collaboration and discourage you from taking too much credit for a team effort.
A resume is a bit different in that while you still shouldn’t take more credit than is deserved the goal is to highlight your qualifications and contributions to the organization’s success. The people reading your resume are thinking of hiring you, not your team, so they care about you and what you can bring and not what your team can.
#3 – Using Too Much Technical Jargon
Finding the right wording can be difficult, and like many disciplines the field of data analytics has its own vocabulary. Because of that many data analyst resumes are littered with technical jargon. However, in many cases that jargon is just an attempt to sound knowledgeable while simultaneously hiding poor communication skills.
The thing is, writing in simple terms is hard. It requires real effort and a solid understand of the subject, but when it is done right it makes your resume really powerful. Keep in mind that in many cases the first person reading your resume is an HR recruiter and not the hiring manager. They likely don’t even fully understand their own data analyst job description let alone the technical jargon. Their inability to understand the technical aspects of your resume won’t impress them, in fact it is much more likely that they will decide to pass in favor for the candidate with a resume they can understand.
So as much as possible write in plain language and skip the technical jargon.
#4 – Listing Too Many Technologies
Another common data analyst resume mistake that I see is listing too many technologies and programming languages. While the logic for why you may be tempted to list every technology you have ever touched is understandable, it in theory would make you a viable candidate for more jobs, in reality it makes you come across as either untruthful or as someone with low self-awareness.
There is no way you are proficient in 25+ technologies. You simply couldn’t have used them enough to gain any sort of mastery. So you either are stretching the truth or you lack the judgement to understand what mastery looks like. Either way it is a major red flag.
As a rule of thumb in order to include a technology you should have at least two practical examples of how you used the technology to solve a problem. This should not include practice problems you did during a training session. If your only exposure is training (school work is OK here) then you should find some practice projects before you put it on your resume.
#5 – Giving Yourself Ratings on Skills
This is another trend that is becoming more common for data analysts. In the skills section of their resume many applicants rate themselves, especially on technical skills like Python, SQL, and Tableau.
They are often encouraged to do this because many resume templates have a section for this. DO NOT DO THIS. Especially if you are an entry-level data analyst candidate trying to get your first data analyst role.
The reason is that you don’t know what you don’t know. You might be an expert in something relative to the work you did in the past, but you are not an expert relative to the senior data analysts that you’ll be sitting next to.
Instead list your skills but do not use any sort of rating system or visualization. If you feel you must highlight your proficiency you can talk about certifications or maybe even years of experience using the skill but don’t rate yourself. If you rate yourself an expert and get stumped on the first interview question you’ll lose all credibility. The reward simply isn’t worth the risk.
#6 – Having Discrepancies Between Your Resume And LinkedIn Profile
This should be a simple one yet many people have this issue out of sheer laziness. Ensure your resume and LinkedIn profile say the same thing. Whether its job titles, years of experience, dates, contact information such as email address, or any other important information. Verify that all of this aligns between the two. If not, hiring managers are going to wonder why they don’t match.
#7 – Using A Resume Format With Too Much Color Or Too Many Visuals
If you go to Google and search for “resume template” you will find a nearly unlimited supply. Many of them are visually catchy, have pleasing colors, and nice graphics.
While there is nothing wrong with these formats you should keep in mind that the vast majority of them were created by people who work in visual design. This means that for them the look of their resume is just as important as the content.
This is not true for a data analyst. The content is critically important and the resume format is marginally important.
It can also annoy interviewers who like to print out resumes. They may feel irritated if they have to use extensive amounts of ink to print your resume. My suggestion is to find a format that looks nice and professional but goes light on color and visuals.
#8 – Submitting Your Resume In A Format Other Than PDF
Not too much to say on this one. While you probably create your resume with Microsoft Word or Google Docs you should always submit your resume in a PDF format unless instructed otherwise. When it goes into the applicant tracking system using a PDF will prevent any formatting issues down the line.
#9 – Not Demonstrating How You’ve Used Data & Analytics To Solve Problems
The best way to prove that you have the right data analyst skills isn’t to tell someone but to show them. Ideally you’ll have previous work experience or school projects that can showcase what you can do.
What many data analysts get wrong is that they think simply having learned about the data analysis process and having a degree is enough by itself. It isn’t. You need to show how you used data analysis to solve a problem, whether that be for yourself or someone else.
Analytics without action is just academics. While there is nothing wrong with academics, the majority of data analysts are going to work somewhere that will require business outcomes. Show that you can use analytics to solve a problem.
#10 – Not Tailoring Your Resume For The Job You Want
There are two times when you need to tailor your resume. First you should create a sort of “base” resume that is written for the type of data analyst jobs that you want. It isn’t tailored for any one specific role but instead for your ideal or target role.
The second time is after you have your base version ready and are starting to apply to actual roles. I generally recommend tailoring somewhere between 5%-10% of the content for each role. While this can seem like a lot it is actually faster in the long run because you are likely going to have to apply for less roles.
The tailoring for each role doesn’t have to be complex, in fact simple is better. In many cases it is as simple as adding certain keywords to your skills section or resume summary paragraph. Other times you may need to add a line to your professional experience section to show that you’ve worked on a particular topic before. Anything that will help the HR recruiter make a connection between your resume and the job posting will increase your chances of getting a call back.
Frequently Asked Questions
Do these same tips apply for data scientists, business analysts, and business intelligence analysts?
Yes. While this post is tailored for what a data analyst will experience every tip on here applies just as much to those other roles.
Why can’t I list all of the technologies I have used before?
If you have experience with them and are comfortable answering questions about them then by all means include them. However you don’t want to be the person who lists everything under the sun because in reality there is no one who actually has all of those skills. They are often referred to as “unicorns” and any hiring manager with some sense is going to realize unicorns aren’t real.
Isn’t it better to cast a wide net than to have a resume that is too narrow or specific?
No, in fact just the opposite is true. So many aspiring data analysts want to come across as someone who could do any role. While this might make sense in theory it doesn’t work in reality. It is human nature to pick the product that seems to most closely solve our need. The same goes with hiring people. If there is a jack of all trades and a specialist who excels in the right relevant skills then most of the time the specialist will be chosen.
Does the format I use for my resume matter?
Generally speaking no, the resume format isn’t critical as long as you don’t use anything too outrageous. What’s more important is having the right section and conveying the right information to tell your story.
What sections should I include in my resume?
A summary section that gives a few sentences about you while also including a subtle objective statement about what you are looking for in a role.
A key skills section that gives 8-12 of your technical skills and other specialized knowledge. This is especially useful for getting past HR folks who are looking for keywords without having to pepper in too much of the jargon in the experience section.
An experience section that breaks down work you’ve done in the past and they key (relevant) accomplishments.
An education section showing what degrees or certifications you hold.
Why aren’t employers calling me back to interview?
This could be happening for any number of reasons but the most likely reason is that you haven’t demonstrated enough potential to do their role well. Go back to your resume and look to see how often you are “telling” vs how often you are “showing”. Also make sure your accomplishments are actually note worthy. Small wins aren’t going to impress anyone. If you want more help in figuring out why then set up a time to chat with me.
Conclusion
Avoiding resume mistakes can go a long way in helping ensure your resume actually gets read by the right people. By following these tips, you will be one step closer to having a top-notch resume that will stand out from the rest and help you land that dream job!
I hope you found this blog helpful and please share it with your friends. Also please consider setting up a meet and greet with me so that we can discuss your career and how to ensure you get the data analyst job that you want and deserve.