3 Data Analyst Job Description Samples & Guide for 2022

Data analysis is the process of turning raw data into information and insight, which can be used to make business decisions. A data analyst, or data business analyst, is someone with the knowledge and skills of data processing software and business development strategies to provide sound decisions to executive management. Data analysts collaborate with other data professionals to identify and extract pertinent data, transform data into useful information, generate reports based on their findings, and monitor key performance indicators (KPIs) to assess business initiatives’ success.

Finding a great data analyst for your organization is a tall order, but BeamJobs is here to help. We have great examples and advice on how to create excellent job descriptions, so you can attract and hire the right people.

How to Write a Data Analyst Job Description

Writing a great job description for any role can be challenging. The data analyst role can be especially difficult because it covers such a broad selection of skills and can be very different among companies. It is easy to fall into the trap of creating a lengthy description of what a data analyst does. 

To ensure that your candidates read and understand the complete description of the job, it is important that you keep it as brief as possible, but make sure you include all the key information that makes your job and/or company unique. Remember, you're selling the job and your organization, and you'll get a lot better quality data analyst resumes and data analyst cover letters when your job description doesn't stink.

You obviously should always include all the pertinent job requirements for the role. Doing this for a data analyst position can be difficult because there are so many things that a data analyst does. 

A great way to show what a job role requires is by using examples of the types of issues a data analyst in your company needs to be able to solve. Use an example of a gap they need to fill or why you are specifically hiring for this role. You can cite a specific current challenge they will need to address. If you are increasing resources, capacities, or capabilities, then talk about that. As always, be specific and brief.

Include critical and unique requirements for your company, but don’t have too many. Avoid listing obvious skills or traits, such as “strong data analysis skills.” A long list of job requirements will discourage diverse candidates from applying. Companies are constantly striving to increase their diversity and remove barriers for women and minorities in the workplace. 

To ensure your job descriptions are not countering these efforts, it’s important to use words and phrases that are not biased. Mya’s “Bias in Job Descriptions” article breaks the topic down to give you a better understanding of how to keep your documents unbiased.

Furthermore, data analysts must manage many complex tasks simultaneously. They must be able to quickly and efficiently identify essential data and distill massive quantities of data down to the most valuable information. Your job description needs to do the same. 

The University of Arizona's writing center stresses the importance of picking "the word that most clearly conveys your meaning." When writing your data analyst job description, consider the connotation of each word choice. While you may want your data analyst to meet with stakeholders, consider leading with "engage" rather than "meet." While they may have similar meanings, "engage" has a more positive connotation.

Finally, before you post your job description for the world to see, take the time to revise and edit. We know you worked hard, so avoid the temptation to submit your description for mass perusal before you’ve corrected word choice, grammatical errors, or even worse, an inaccurate salary range with one too many zeros. 

So, take an extra day or so to have other staff (preferably data analysts familiar with the jargon) provide feedback; then, give it a last look yourself. When you’re satisfied, submit your post to boards with confidence, knowing that your job description will speak to high-quality talent.

Your data analyst job description layout

Below is an outline that covers all the items for all data analyst job descriptions. 

Job details: This is a quick introductory section for your job opening and company. Take advantage of this section to immediately sell the role and your organization. Remember to keep it very brief. Give a quick one or two sentences about the what and why of the data analyst role. It is also important to give them a taste of what your company is about. You want to strongly entice them to join your company.

What you’ll be doing: This section may also be referred to as “Roles” or  “Responsibilities” or “Requirements.” This should be a bulleted list of the tasks involved in the job role. As we mentioned earlier, keep this list short, but make sure you include anything unique to the data analyst role in your company (versus other companies) and critical to the success of the role. Best practice guidance for any job description is to always use active verbs, avoid jargon and filler words, and be as specific as possible.

Qualifications: This is a key section of the job description because it quickly tells the applicant whether they are a good fit for the role. It is critical to include all the absolute must-haves for the position. Typically, these are things like: education, experience, certifications, and specialization requirements. 

For soft skills, use relevant personality words like “creative,“ “self-motivated,” “organized,” and “reliable,” but avoid having too many. Data analysis has a lot of typical requirements, but it also covers a wide range of skill sets. Always include any special and non-standard qualifications for your company. As with every section of your job description, be brief, specific, and clear.

Benefits: Placement of this section is flexible. We don’t recommend having it as an introduction, but it is okay just about anywhere else. You may want to have this early in your job description if your benefits are a major selling point for the position. Focus on anything that differentiates your company benefits from other companies. Be specific and clear about the benefits and list everything. Again, you want to make the position as attractive as possible.

About the company: We recommend placing this at the very end of your job description. This section is where you give job seekers more details about your company. If your company is small and not well known, this is where you tell them why they should want to work there. 

A data analysis professional is also interested in knowing what the company’s position is on leveraging information technology and big data. You should, in a sentence or two, give them a brief synopsis of how the company approaches the use of data in their strategy and how the data analyst role fits in.

Data analyst roles and responsibilities

Data analysts are investigators and puzzle solvers. They seek out data that is relevant to the business strategies in any way, and they identify patterns and trends in the data that can be translated into viable business plans and/or tactics. The data analyst must be able to process and translate very large volumes of data into understandable and usable business information.

Below are some sample roles a data analysis professional might fulfill as part of their job. One single data analyst might not fill all these roles, but this will serve as a source for things you might want to include.

Data Acquisition

• Great data analysts have an investigative nature that allows them to review large databases and data sets from many sources to extract pertinent information in response to a particular question.
• Identify internal and external sources of data that can be used as well as to detect important patterns and trends in the data.
• Determine the processing methodologies to best transform data into usable business intelligence.
• Think critically about how data is used, and assess and quantify strengths, weaknesses, and opportunities posed by data sources and processing.
• Continually seek to find new data sources, ways to improve data processing, and new technologies to better utilize data.

Problem-solving

• The data analyst function helps to define how data can be used to make the organization successful. They provide expertise and guidance to the business on how to best use data to drive processes to support the business strategy.
• Study business challenges, research relevant data processing techniques, and determine solutions (leveraging data analysis).
• Build, maintain, and enhance technology systems (databases, warehouses, interfaces, reporting, etc.) for obtaining and processing data.
• Identify technical issues in data acquisition, processing, and utilization, and assess and resolve any issues found.
• Continually work with the business to review and assess business processes about how data is gathered, analyzed, and used.

Communication and Reporting

• A successful data analyst must have excellent written and verbal communication skills that enable them to present complicated information and findings to all levels within the company.
• Collaborate with teams, colleagues, departments, and all other stakeholders to effectively understand business processes, and translate technical information into coherent business information.
• Compile written analyses and reports regarding certain data figures.
• Effectively translate complex data and findings into useful information for all business stakeholders.
• Facilitate business communication and reporting for internal and external stakeholders.

Leadership

• The best data analysts have a very team-oriented mindset and work extremely well with other employees to find answers leveraging data.
• Work with business owners to understand business processes in great detail.
• Drive data analysis efforts as a subject-matter expert, team leader, and process owner.
• Consult with and guide all stakeholders (internal and external) regarding best practices and effective usage of data to improve business processes.
• Lead teams and projects to productively leverage data gathering and analysis to successfully complete tasks, initiatives, and projects.

Training

• Data analysts should demonstrate a strong interest in continued education and seek certification courses related to statistics or data analytics to improve their professional capabilities. Technology, data sources, and related best practices are constantly changing and evolving.
• Continually research and stay up to date on the current state of data analysis.
• Maintain professional training hours, and keep certifications current.
• Engage with the business and stay in tune with what stakeholders are doing with the data, information, and reporting provided to ensure that business needs continue to be met.
• Promote ongoing training and technical education for data analysis professionals and business process owners to enhance and evolve the organization’s utilization of data.