Updating Your Data Resume: Tips from a Former Resume Writer

Examples to help improve your data resume.

Prior to working as a data engineer, I was an academic consultant and reviewed hundreds of resumes for Pearson Education and Arizona State University.

Lately, I’ve received requests to help friends who work in and outside of the tech field with their resumes. The following story is a condensed version of tips I offer, with a particular focus on landing a job in data. I hope you find them helpful.

Your First Job: Getting Your Foot in the Door

Following the Harvard Business Review’s 2012 proclamation that data science is the sexiest job of the 21st century, a niche industry received widespread media attention, soon spawning buzzwords like ‘big data.’ In the past ten years, thousands of early or mid-career professionals saw an opportunity to gain technical skills relatively quickly (thanks to the prevalence of MOOCs), earn a six-figure salary and build cool stuff. Individuals pivoted in droves.

I was one of them, having begun my career in journalism and media.

Even though data science job openings increase significantly each year, the problem is that the competition does too. Nearly 50% of data science candidates have a phD. Nearly 80% hold a master’s degree. To be completely candid, these are significant barriers to entry for anyone without advanced academic credentials, a body of published research work or proficiency in multiple programming languages.

As a data hopeful this is who you’re competing with.

That’s why heeding my first tip is incredibly important.

Stop Being Precious About Your Resume

I have a master’s degree. I studied data science. I had a robust GitHub portfolio and I frequently wrote stories here on Medium.

I still applied to over 100 jobs in the data field, spreading my applications between open data analyst, data science and data engineering positions.

I heard back and interviewed with 10–15. That’s approximately a 10% success rate for weeks’ worth of daily applications.

In my experience, job applications, even in a candidate’s market, are a volume game.

Instead of viewing your resume as a precisely crafted document that you’re sending to 5–10 companies, think of it as a template that you’ll use to send out to 3–5 jobs per day.

Become comfortable adding or cutting entire job descriptions, years of your professional life, out of your resume for the sake of clarity. If you’re applying to a data position there is hardly any need for any of the following sections:

• Volunteering
• Hobbies
• Certifications

Truly, the only information you need to include on a technical resume is:

• Education
• Professional Experience
• Technical Skills
• Relevant projects (if you have the room)

In an industry that prides itself on the application of logic and concise, readable blocks of text, you want to avoid anything that distracts, overwhelms or otherwise gets you thrown in the ‘no’ pile.

Tech experts agree that a certification section plastered with certificates with every last Udemy or Coursera course you finished conveys overcompensation instead of confidence. Instead, the impactful certifications are those endorsed by companies like Google (GCP), Microsoft (Azure) and Amazon (AWS).

Turn Your Job Descriptions into Job Wins

I read a non-data, non-tech friend’s resume recently that read like a job description. This is the exact opposite of what you want to submit.

Conversely, think of your resume as a highlight reel of professional accomplishment. Since you should only include two to four bullet points per job listed, you will want to seriously consider the content you include.

Instead of writing something like ‘provides excellent customer service to stakeholders’, provide evidence that points to organizational communication as being an impactful part of your experience at your soon-to-be former company. To accomplish this, you’ll need evidence. Just like you’d include a page number or academic citation when writing a research paper, you’ll want to include evidence that supports your claims of professional excellence.

For the previous customer service example, I’d rephrase that sentiment to: ‘consistently exceeds customer service expectations, scoring 5s on quarterly internal surveys.’

Here are other examples of data-oriented job wins:

• Significant model/pipeline/query performance increases
• Automating a previously tedious manual process
• Leading visible projects
• Earning an award, i.e. employee of the month (a bit old-school, but still demonstrates dedication and achievement)

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Quantify Experience

Since you’re pursuing or transitioning to a data job, numbers speak louder than words. One of the best ways to demonstrate your professional value is to quantify the impact you’ve had on your team and organization at large.

Even providing a ballpark estimate can help assure a recruiter or hiring manager that hiring you will result in measurable outcomes for the organization. The following examples will demonstrate the power of offering concrete examples of performance.

For Data Scientists

• “Increased accuracy/precision of x model by 15%”
• “Created vectorized corpus for 10 million Tweets”
• “Developed and fitted 55 features to x model”

For Data Analysts

• “Decreased dashboard load time by 1 min”
• “Increased performance of 100 scheduled queries”
• “Delivered report to support product that generates 10 mil. in ARR”

For Data Engineers

• “Deleted outdated VM instances, saving company $10,000 in annual cloud computing costs”
• “Built 15 ETL pipelines supporting the transfer of 10 TB of data”
• “Oversaw the creation and expansion of a 250 TB data warehouse”

Each of these statements answers the question: What professional value will this candidate add to the organization? This question lingers in a recruiter’s mind from the moment they open your application.

Contextualize Technical Expertise

Although you might be tempted to keyword stuff your resume by including every last recommended or required technical skill from the application, contextualizing your technical expertise will help a reader understand how you apply your vast skill set.

Truthfully, even though job descriptions say ‘must have x amount of expertise in Python’, what they’re really asking is for you to demonstrate how you used Python academically, professionally or personally. Even though it might seem daunting to condense your many years of programming skills into one bullet point, this can also spawn a larger conversation during a job interview.

In an interview I was asked about a Spark ETL pipeline I built in grad school. Why did I encounter that specific question? Because I listed the skill on my resume instead of burying my experience with Spark at the end with a litany of other technical abilities.

The takeaway: Even though you may be acquiring or refining so-called marketable skills, they will not serve you well until you can articulate how you apply them to business problems and, preferably, in a way that demonstrates quantifiable value to your future employer.

Most of the above points are pieces of advice I’ve been giving to technical and non-technical job seekers for years. I still have more suggestions but understand you have limited time. Feel free to comment if you have specific questions or ideas for topics you’d like me to cover in a follow-up story.

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