Data analysts must use a variety of skills and tools to provide their organizations with accurate analyses of (often massive) datasets. It’s a demanding job, but also a potentially lucrative one. Given those pressures and potential rewards, how can you tailor a data analyst resume so you really stand out to recruiters and hiring managers? Let’s look at the “ideal” data analyst resume template.
First and foremost: the ideal data analyst resume demonstrates that you know how to interpret data in ways that yield meaningful results for organizations. Beyond that, it should go into granular detail about your skills in implementing data analysis procedures, maintaining and improving databases, and identifying areas where data may be of best use. Let’s jump in!
Data Analyst Resume Template
Curious about what a data analyst resume should look like? Check out this example!
Highlighting Key Statistics is Ideal
“A hallmark of a good data analyst is the ability to ultimately mitigate risks in the business and use data sets to improve customer satisfaction,” said Tom Bolek, vice president of platform presales at data management firm Ataccama. “Demonstrating key statistics outlining the efforts to reach these goals is ideal.”
Any resume should be neatly organized and easy to read, with key highlights available at a glance. “The ability to have a polished resume easily indicates key organization skills necessary for this type of role,” Bolek added. “Most platform providers offer solutions that encompass easy to use functionality for better understanding of any given data set.”
Data analysts need to be critical thinkers, with the ability to recognize patterns as well as the data’s “larger story.” When crafting your resume, it’s key to show how your analysis translated into results for the company; while you can’t always go into granular detail thanks to the need to protect your former employer’s proprietary data, you can use other metrics to suggest success (expressing division growth as a percentage, for instance).
List Database, Risk Management Skills
Bolek said logic and analysis, data mining, database management, and risk management are among the key skills a candidate needs to possess, as they demonstrate the data analyst’s capacity for thinking critically. If you have it, you should also mention any experience in system administration, data warehousing, regression analysis and business intelligence (BI)—these are all vital skills at many companies.
“Data visualization is another key component and is a growing part of the data science industry,” Bolek added. “Any skills around data visualization, and how such work has impacted projects in a positive way, should be listed under key career accomplishments in past employment.”
Take Note of Shifting Responsibilities for Data Analysts
Kyle Kirwan, now CEO and co-founder of data observability platform Bigeye, began his career as a data analyst. He suggests that, as data becomes embedded in more and more aspects of how modern businesses operate, the role of the analyst is shifting—and so is the ideal skill set.
A data analyst’s skills can be grouped into a few core categories, he explained. The importance of each category is changing as businesses evolve in how they work with data. From Kirwan’s perspective, here are key skills to include on any data analyst resume:
- Engineering skills such as querying data and transforming data; also, platforms and programming languages in which you have experience, such as SQL, R, Python, or data engineering frameworks.
- Analytical skills (including statistical methods and visualization techniques) that show you understand a/b testing essentials, when to use different statistics (like means vs. medians), and what types of visualizations work best in different scenarios.
- Business knowledge (such as go-to-market strategy and KPIs). You want to prove you understand how a business operates, what does and doesn’t matter for decision-making, and the terminology and metrics common to the area of analysis.
- Stakeholder management, including audience types and anticipating questions. Businesses are looking for data analysts who understand who is consuming their insights, the decisions that need to be made, what that person needs to understand to make a good decision, and how to anticipate any follow-up questions.
Analysts no longer simply pull data, draft a one-off report, then stand and deliver it to an exec in a meeting, and repeat next month,” Kirwan said. “They’re now expected to apply analytics methods in more scalable ways, and to use the time they get back to better leverage their business understanding and stakeholder management skills.”
Demonstrating Your Drive and Leadership
Any recruiter or hiring manager is going to want a sense for how big a challenge you can take on, how much impact you can drive, and whether your experience can map to the challenges to be solved at their company.
“Your career history should aim to highlight what the challenge was that you helped the business get through, the work you did to get them there, and what the quantified outcome was—as much as you’re allowed to share it,” Kirwan said. This includes listing things like optimizing the annual marketing budget, perhaps by developing a new KPI that exposed an inefficiency in the mix of channels, which then led to an improvement in conversion for the funnel step of interest.
Because data analysts today are expected to have a wider breadth of technical, analytical, and business knowledge, candidates should also highlight concrete experiences and roles in each of these areas.
“For example, when I was at Uber, one of my roles, which was core to the business, was analyzing our signup funnel—at what point in the signup process do users usually drop off?” Kirwan said. “I also provided data about which drivers had very low ratings to the operations team. Much of this work was still mainly reporting, but it was essential to figure out how to present data, not just pull it, and then how to get it into charts or visuals so that the entire team could consume that information.”
Bring Attention to Metrics-Based Performance Indicators
Candidates should include 1-2 notable examples that help hiring managers evaluate their level of responsibility and experience and how they contributed to the business. “While data analysts can’t usually claim that they were responsible for results like selling four times more widgets, the analyst’s goal is to help the organization make better decisions that lead to better outcomes,” Kirwan said. “So, the best way to highlight these types of accolades is to describe the organization’s goal and how much business value was riding on that goal.”
For example, that could include the kinds of insights they uncovered that allowed the business to make its goals or improve outcomes. “Maybe you invented a predictive KPI that the business used to remove fraudulent accounts, and that contribution led to the removal of 227,000 fraudulent accounts over the calendar year,” he proposed.
In that example, a data analyst could highlight:
- How they created a predictive KPI.
- The technical skills used in this effort.
- Who used the KPI and for what (i.e., the ops team used it to remove 227,000 accounts during the year).
- The business impact, providing concrete numbers whenever possible.
“Highlights like this help hiring managers understand what you did specifically and how it produced value for the business,” Kirwan said.