UPDATED BY
Jessica Powers | Sep 07, 2022

The terms may sound similar, but data analysts, data engineers and data scientists are actually very different roles.

Here’s the simplified version: Data analysts are responsible for collecting, cleaning, analyzing and reporting data; meanwhile, data engineers create and maintain architectural systems for collecting, storing, analyzing and managing large quantities of raw data; and finally, data scientists handle data collection, analysis and visualization — and sometimes build things like machine learning models.

To write a stellar job description for data analysts — and attract top candidates — you’ll need to understand their role more specifically. That’s where this guide comes in.

Table of Contents

  • What Does a Data Analyst Do?
  • Types of Data Analyst Jobs
  • Data Analyst Skills 
  • Data Analyst Salary Information
  • Data Analyst Job Description Template

 

What Does a Data Analyst Do?

Data analysts gather data across a business, analyze it and translate the results into non-technical language for team members of all backgrounds.

Data analysts are typically early in their careers and may be seeking their first job after completing a bachelor’s degree or gaining the equivalent professional experience. Common degrees include statistics, math, computer science, physics, finance, business administration, economics or a related field.

What Is a Data Analyst?

Data analysts gather data across a business, analyze it and translate the results into non-technical language for team members of all backgrounds. They sometimes track web analytics and use data analytics tools to discover trends that can influence the decisions made by an organization.

Data analysts may be responsible for building data models to organize important data for different teams across the business and for monitoring and handling data. With large quantities of data comes endless possibilities for mistakes, requiring data analysts to constantly be on the lookout for information that needs cleansing and updating.

In addition to gathering, analyzing and cleansing information, data analysts create business reports for teams and individuals across the business. They also help translate analytics into non-technical insights to help all teams make well-informed decisions based on empirical evidence.

As they progress in their careers, data analysts may continue their education and become data engineers and eventually data scientists.

 

Types of Data Analyst Jobs

There are many different careers and jobs that data analysts can hold. Some of the most common fields for data analysts to work in include healthcare, big data, market research, operations and intelligence. 

Let’s take a closer look at a few different types of data analyst jobs and what they do. 

 

1. Business Intelligence Analyst

The primary job of a business intelligence analyst is to extract valuable insights from company data. Someone in this role should be comfortable with SQL, analyzing data, as well as creating data models.

 

2. Marketing Analyst 

Marketing analysts help their team track the success of campaigns by using Google Analytics, custom reporting tools or other traffic analytics sites to determine the impact advertisements are making. Marketing analysts are key to marketing departments as they help understand what efforts are working and what advertisements to spend company money on. 

 

3. Transportation Logistics Specialist

Transportation logistics specialists can utilize a data analytics background in a variety of ways. This role relies heavily on the ability to identify efficient delivery routes for products and services. Someone in this role uses large datasets to eliminate transit bottlenecks. 

 

4. Operations Analyst 

An operations analyst’s primary job is to organize a company’s internal processes. This role focuses on general operations as well as internal reporting and product manufacturing and distribution. Operations analysts can work for nearly every type of business, including supermarket chains, delivery providers or even government agencies. 

 

5. Healthcare Analyst

Healthcare data analysts collect, organize and interpret data from sources like electronic health records, billing claims, cost reports and surveys. The purpose of this role is to assist healthcare providers in order to improve the quality of care, lower costs and improve patient experiences. Someone in this field might have duties like automating internal and external reports, creating data dashboards or being responsible for presenting information to hospital executives. 

Related ReadingData Analyst vs. Data Scientist: Similarities and Differences Explained

 

Data Analyst Skills

Data analysts employ a variety of soft and technical skills throughout their careers. Like many positions, having clear communication skills and the ability to present complex information is crucial to this role. Critical thinking skills are an essential part of many jobs, and data analysts are no exception. These soft skills are especially important to data analysts because they are often responsible for presenting data to stakeholders and other teams in ways that everyone can understand. 

Along with communication and critical thinking skills, data analysts will need to understand different visualization tools, coding languages and mathematical principles.

Top Data Analyst Skills

  • SQL
  • Data visualization
  • Data cleaning
  • Critical thinking
  • Python
  • Communication

 

Coding Languages

Mastering coding languages like R and Python is important as they are standard in the industry. These languages also provide advanced analyses and predictive analytics on large data sets. Some coding languages data analysts need to know are: 

  • C#
  • Git
  • Javascript
  • .Net
  • Python
  • R

 

Data Visualization

A key element of a data analyst’s job is data visualization. Data visualization allows analysts to identify patterns and showcase their findings to stakeholders and other teams. This skill is crucial in shaping company decisions and roadmaps. Some data visualization tools that data analysts use include: 

  • Google Analytics & Google Tag Manager
  • Looker
  • Microsoft Power BI
  • Superset
  • Tableau

 

Databases

Data analysts rely on databases to store, maintain and organize data. There are several types of database languages that analysts may need to learn early on in their career. SQL, one of the first database languages created in 1970, is still a standard for querying and handling data today. Some common database languages for data analysis include: 

  • Apache Cassandra
  • MariaDB
  • MongoDB
  • MySQL
  • Node.js
  • PostgreSQL
  • Redis

 

Data Warehouses

Data analysts use data warehouses to perform queries and analysis on historical data. The information contained in a data warehouse can include data such as application log files and transaction applications. These tools are useful to analysts because they consolidate large datasets from many sources. Often called a “single source of truth,” a data warehouse allows a company to improve decision making based on historical insights over time. Some types of data warehouses are: 

  • Amazon Redshift
  • Apache Hive
  • Microsoft Azure SQL Database
  • Oracle Database
  • Oracle Warehouse Builder
  • SAP NetWeaver Business Warehouse
  • Snowflake

 

Data Analyst Education Requirements

Although it may be possible to get a job in data analytics without a degree, having a bachelor’s degree can help candidates stand out and is often a requirement for many positions. Majoring in data analytics in an undergraduate program is a great place to start but not all universities offer this. Some alternative majors to look into include data science, computer science, applied mathematics or statistics. 

Whatever major you choose, taking courses on statistics, calculus and linear algebra will help you develop crucial skills for your career. Computer science courses with a focus on databases and statistical software will also provide a solid background to draw from. For those that have an idea of what field they’d like to work in, it’s always a good idea to take a course or two in a specific industry like healthcare or finance. 

Obtaining a master’s degree in analytics or a related field will open up more opportunities as well as senior positions. In fact, approximately 50 percent of professionals in the data science and analytics industry hold master’s degrees. Master’s degrees can help data analysts advance their visualization skills, understand how to use data in an ethical way and learn the best practices for data security. 

More on Job DescriptionsHow to Write a Job Description: Data Driven Results

 

Data Analyst Salary Information

To help determine what candidates expect, we’ve gathered average data analyst salary information from seven major hiring markets in the United States.

 

Data Analyst Job Description Template

Below are some resources to help you write a job description that will attract candidates with the skills needed to be successful in their role. It includes a data analyst job description template for you to alter and customize so that it includes the necessary responsibilities and requirements while reflecting your unique company culture. 

 

Company Bio

Use this section to provide a high level overview of your company, culture, perks and benefits, career development opportunities and anything else that will get candidates excited about your company.

 

Responsibilities

  • Collaborate with various stakeholders and teams including product, engineering and finance.
  • Provide teams and stakeholders with actionable insights and analysis reports based on data to support decision making efforts.
  • Collect data from numerous data sources, clean data and analyze data to identify trends.
  • Build and analyze automated dataset dashboards to predict issues before they arise, identify bugs in data and resolve them.
  • Support individual team members by creating customizable tabular or visual reports with ad hoc reporting via SQL.
  • Communicate and present technical information with non-technical team members and stakeholders.

 

Requirements

  • Bachelor’s degree in computer science, mathematics, finance, economics, statistics or a related field.
  • [X] years experience working in technical data analysis, data science, data warehousing in [insert industry] or a related industry.
  • Experience with designing reports and dashboards on [insert tools].
  • Experience with [insert relevant databases].
  • Strong knowledge of [insert coding languages].
  • Excellent communication skills including written, verbal and presentation.

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