Template, tips and things to include

Data analysts must use a variety of skills and tools to provide their organizations with accurate analyzes of (often large) datasets. It is demanding work, but also potentially lucrative. Given these pressures and potential rewards, how can you tailor a data analyst resume to really stand out to recruiters and hiring managers? Let’s look at the “ideal” data analyst resume template.

First and foremost: An ideal data analyst resume demonstrates that you know how to interpret data in ways that produce meaningful results for organizations. Beyond that, it should go into granular detail of your skills in implementing data analysis procedures, maintaining and improving databases, and identifying areas where data may be the most useful. Let’s go!

Data analyst resume template

Curious about what a data analyst resume should look like? Check out this example!

Highlighting key stats is ideal

“The hallmark of a good data analyst is their ability to mitigate risk across the business and use datasets to improve customer satisfaction,” said Tom Bolek, vice president of platform pre-sales at the company. Ataccama data management. “The ideal is to present key statistics describing the efforts made to achieve these goals.”

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 the key organizational skills needed for this type of role,” Bolek added. “Most platform vendors offer solutions that encompass easy-to-use features for better understanding of a given dataset.”

Data analysts must be critical thinkers, with the ability to recognize patterns as well as the “bigger story” of data. When writing your CV, it is essential to show how your analysis has translated into results for the company; although you can’t always go into granular detail due 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 example ).

List database, risk management skills

Bolek said that logic and analysis, data mining, database management and risk management are some of the key skills a candidate should possess, as they demonstrate the analyst’s ability to data to think 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 essential skills in many companies.

“Data visualization is another key element and is a growing part of the data science industry,” Bolek added. “Any skills related to data visualization and how this work has positively impacted projects should be listed in the major professional accomplishments of previous jobs.”

Take note of the changing responsibilities of data analysts

Kyle Kirwan, now CEO and co-founder of data observability platform Bigeye, started his career as a data analyst. He suggests that as data becomes embedded in more and more aspects of modern business operations, the role of the analyst is changing, and so is the ideal skill set.

The skills of a data analyst can be grouped into a few main categories, he explained. The importance of each category evolves as companies evolve in the way they work with data. From Kirwan’s perspective, here are the key skills to include in any data analyst resume:

  • Engineering skills such as data querying and data transformation; 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 the essentials of a/b testing, when to use different statistics (like means versus medians), and what types of visualizations work best in different scenarios.
  • Business knowledge (such as go-to-market strategy and KPIs). You want to demonstrate that you understand how a business works, what matters and what doesn’t matter for decision-making, and common terminology and metrics across the analytics domain.
  • Stakeholder management, including audience types and anticipation of questions. Companies are looking for data analysts who understand who is consuming their information, what decisions need to be made, what that person needs to understand to make a good decision, and how to anticipate follow-up questions.

Analysts are no longer just pulling data, writing a single report, then getting up and handing it to an executive in a meeting, and repeating the next month,” Kirwan said. “They are now expected to apply analytical methods in a more scalable way and use the time they recover to better leverage their understanding of the business and their stakeholder management skills.”

Demonstrate your dynamism and your leadership

Any recruiter or hiring manager will want to get an idea of ​​the scale of the challenge you can take on, the impact you can have, and whether your background can match the challenges at their company.

“Your career history should aim to highlight the challenge you helped the company overcome, the work you did to get there, and the quantified result, as long as you are allowed to share it,” a Kirwan said. . This includes the list of things like optimizing the annual marketing budget, perhaps developing a new KPI that revealed an inefficiency in the channel mix, which then led to a conversion improvement for step d. funnel interest.

Since data analysts today are expected to have a broader range of technical, analytical, and business knowledge, candidates should also highlight concrete roles and experiences in each of these areas.

“For example, when I was at Uber, one of my roles, which was core to the business, was to analyze our sign-up funnel. At what point in the sign-up process do users typically drop out? ?” Kirwan said. “I also provided data on drivers who had very low ratings to the operations team. Much of this work was still primarily reporting, but understanding how to present the data, not just extract it, and then how to integrate it into charts or visuals so the whole team could consume that information was key.

Draw attention to metrics-based performance indicators

Applicants should include 1-2 notable examples that help hiring managers assess their level of responsibility and experience and how they have contributed to the company. “While data analysts typically cannot claim they were responsible for outcomes such as four times as many widgets being sold, 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 distinctions is to describe the purpose of the organization and the business value related to that purpose.”

For example, this could include the types of information they uncovered that helped the company achieve its goals or improve its bottom line. “Maybe you invented a predictive KPI that the company used to remove fraudulent accounts, and that contribution led to the removal of 227,000 fraudulent accounts in the calendar year,” he said. propose.

In this example, a data analyst might highlight:

  • How they created a predictive KPI.
  • The technical skills used in this effort.
  • Who used the KPI and for what (i.e. the operations team used it to delete 227,000 accounts during the year).
  • Business impact, providing hard numbers where possible.

“Highlights like this help hiring managers understand what you did specifically and how it drove value for the company,” Kirwan said.

Sean N. Ayres