Crafting a Resume to Land the Job of Your Dreams
Data analyst jobs are forecast to see a 25% rise from 2019-2019, according to the BLS. This rapid growth puts demand for skilled data analysts way above the average for other jobs in the United States. With such a large growth in demand, you have the chance to land the job of your dreams.
However, the high demand for data analysts does not mean you don’t need to put in the effort. Competition for top jobs is tough, and to stand out you must have a killer resume.
Here are seven mistakes to avoid when crafting a resume for a data analyst job.
1. Misrepresenting Your Experience
When applying for data analytics jobs you should tailor your resume to the position, by including relevant experience and qualifications and highlighting this by using keywords from the job description. This is a great tactic, but do not be tempted to misrepresent yourself.
Don’t exaggerate your experience in certain industries or with certain technology or tools. When questioned in the interview, it will quickly become apparent that you misrepresented yourself.
2. Resumes Longer Than 3 Pages
Resume length matters. Three pages should be the maximum, though it is best to aim for less. Of course, if your relevant work experience warrants three pages, then you won’t be penalized.
However, if you have only a couple of years of experience and create a resume with lots of padding, this will deter hiring managers. You don’t need to include your high school jobs, irrelevant college credits, or write five paragraphs about a successful project you worked on (bullet points are great for detail).
3. Listing Outdated or Inactive Certifications
Tied in with the above point, don’t include unnecessary qualifications on your resume. Data analytics is a constantly changing industry. New software and processes are being developed every day. You can demonstrate that you regularly keep on top of this with new training, but you don’t need to include training from a decade ago, or with software that has become obsolete.
4. Not Demonstrating Actionable and Quantifiable Results on Your Resume
If your employment section simply looks like a copied and pasted job description, employers will skip right past you in the resume pile. You must demonstrate your value to your previous employers.
Don’t simply list your roles and responsibilities. Explain projects you worked on and how they impacted the company or clients. Give quantifiable results, include numbers, and show how you personally made an impact.
5. Creating a Generic Resume
If you create a resume that is too generic employers will know you are simply sending out the same resume to every data analytics job you see. They will think you haven’t read the job description or researched the company – and will consider that you simply want a job for the money.
Employers want to hire talented candidates who see their future with the employer, so always tailor your resume to the job and the hiring company.
6. Spelling/Grammatical Errors
Spelling and grammatical errors are sloppy. Data analytics jobs require a high level of attention to detail. If you can’t create a resume without errors, how are you going to bring the level of precision needed to the position?
7. Formatting Errors
Much like spelling and grammar, poor formatting indicates that you have not put the time and attention necessary into your resume. This reflects badly on how you will approach your job.
Using small fonts, large fonts, non-standard margins, colorful text, or unnecessary images does not portray the professionalism your resume must. The job ad may also tell you the format in which to produce and submit your resume (e.g. ‘a PDF file no longer than pages’).
To Sum Up
Resume writing is a tricky balance to including everything you must but not overdoing it. It requires precession, and is the first impression you make on a hiring manager. Get it right and it will do the job it should – get you a job interview and make you a star candidate. Follow the above tips and your resume will help you stand out from the crowd.