Data Analyst Vs. Data Engineer: Differences And Similarities

Updated 23 March 2023

If you have an analytical mindset and an interest in working with, and handling data, a career as a data analyst or data engineer may be rewarding for you. Despite their differences, individuals in data science may use these terms interchangeably. Learning more about each role helps you understand what position aligns with your skills, passions and professional goals. In this article, we explain the roles of data analyst vs. data engineer, discuss their differences and similarities, and provide some FAQs to help you decide between these careers.

Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.

What Is A Data Analyst Vs. Data Engineer?

Understanding what data analyst vs. data engineer roles entail can help you decide on a position that suits your career goals. The following describes each role to help you learn more about them:

What is a data analyst?

Data analysts review and collect large amounts of data, such as sales numbers, market research, linguistics and logistics, from various sources for employers. Using their technical expertise, they ensure the quality and accuracy of data. They design, process and present data in ways that help people, and organisations comprehend the data. Data analysts transform data into information that companies use for making better business decisions.

What is a data engineer?

Data engineers use advanced technology to collect and utilise large volumes of data. These engineers build and modify systems that receive information before converting the data into a readable format for data analysts and scientists. They employ various tools and techniques to construct a framework that prepares the information. Data engineers develop data pipelines that combine information from varied sources. These engineers aim to make data more accessible, so companies can use it to evaluate and optimise their performance.

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Differences Between A Data Analyst And A Data Engineer

Understanding the differences between a data analyst and a data engineer is important for choosing a career path. The following are some significant differences between the two roles:

Job duties

The routine tasks of data analysts and data engineers differ because their areas of data management are not the same. The following are some job duties for a data analyst position:

Completing analysis using statistical methods
Converting data into understandable information
Monitoring data to understand industry changes
Converting numbers and figures into narratives that answer business questions
Preparing graphs and reports to share findings with different stakeholders
Supporting initiatives for data normalisation and integrity
Troubleshooting the reporting database environment
Providing expertise in data mining, cleansing and storing

The following are some responsibilities for a data engineer position:

Building data pipelines, algorithms and systems
Collecting data from numerous sources
Conceptualising and generating infrastructure, which allows for analysis and access of big data
Reformulating existing frameworks to optimise their functioning
Creating analytical tools and programs
Liaising with web designers, architects and data analysts
Integrating data and business operations into a data system
Automating management processes and conducting manual data analysis

Career paths

As you gain experience, you may encounter opportunities to advance your career in different directions. As a data analyst, the possible career paths you may pursue include the following:

Data scientist
Analytics manager
Director of analytics
Business analyst
Operations analyst
Marketing analyst
Systems analyst
Healthcare analyst
Data analytics consultant

When you choose a position as a data engineer, you start as a junior data engineer. With experience, you progress to a senior data engineer role. Some career paths you may pursue include the following:

Chief data officer
Manager of data engineering
Data architect
Data science engineer

Focus areas

Data engineers primarily focus on database architectures and develop pipelines that integrate multiple data repositories, and third-party data sources within the company. They work with data lakes, cloud platforms, data warehouses and on-premise technology. Data analysts have an interest in numbers and create optional reports based on enterprise data by using different visualisation tools.

Analysis techniques

Data analysts take actions that affect the company's present scope. In contrast, data engineers create platforms on which data analysts can work. While data analysts use static modelling and descriptive analysis techniques for summarising data, data engineers develop and maintain data pipelines. Data analysts also perform tasks such as data cleansing, analysing and visualising. Alternatively, data engineers focus on building and optimising platforms, and frameworks.

Similarities Between A Data Analyst And A Data Engineer

Understanding the similarities between a data analyst and a data engineer is important for selecting what career path is right for you. The following are some significant similarities between the two roles:

Education

The educational background for both roles is quite similar. Data engineers and data analysts require a degree in computer science or information technology. Both data engineers and data analysts can have a background in computer science engineering. Data analysts often start their careers in mathematics and science, such as a Bachelor of Science. Data engineers may choose software engineering for their bachelor's degree.

Programming languages and tools

Data engineers and data analysts use various programming languages, and tools to carry out their tasks. For example, they use tools such as Hadoop, which helps them structure various big data types for further analysis. They even require knowledge of Kubernetes, which automates the operational tasks of container management and Java, which helps develop enterprise software solutions.

Both roles require knowledge of R, which is an open-source programming language that helps visualise and analyse data. Employers also expect these individuals to know Python, which helps in advanced numerical analysis. Some other tools widely used by data analysts include Power BI, Excel, Structured Query Language (SQL) and Extensible Markup Language.

Skills

The following are some skills that help both indiviudals excel in their roles:

Analytical skills: As data analysts and data engineers study large amounts of data, employers prefer candidates with excellent analytical skills. This ability helps examine the information and collect data from numerous resources.
Communication: Communication is important for discussing technical concepts with non-technical individuals, such as the marketing and sales departments. Effective verbal and written communication skills help create informative reports, and convey findings to the organisation and stakeholders.
Attention to detail: Data analysts use attention to detail to analyse and make conclusions from data. Alternatively, data engineers use this skill set to sort, interpret and process data efficiently.
Critical thinking: Successful data analysts and data engineers can analyse data objectively to produce accurate evaluations. These individuals take a systematic and logical approach to problem-solving.

Work environment

Data engineers and data analysts work in offices or share a workspace with other data employees. They both spend most of their time working with computers or collaborating with colleagues and other data experts. Data analysts and engineers work in nearly every industry, but their most common employers include consulting agencies, information technology (IT) companies, social media agencies, marketing agencies, healthcare organisations and retailers. They typically adhere to regular office hours, but they may work overtime or on weekends, depending on their workload.

FAQs

The following are some FAQs about data engineers and data analysts:

How similar are these roles to data scientists?

Both roles are quite similar to a data scientist role, although a data analyst position is slightly more comparable. Both roles emphasise statistics and use Excel, and SQL for analysis. Like data engineers and data analysts, data scientists may use Python, R and other similar tools for their work. They also have related educational backgrounds, such as computer science, IT engineering and mathematics.

Are there any certifications for these roles?

If your formal education does not relate to data analysis or data engineering, you can acquire these skills through courses and certifications. There are courses for both roles, and many prominent companies that employ data engineers, and data analysts offer them. The certifications you choose depend upon your skills and experience, so conduct some research to assess what certification or course would benefit you.

Do these roles work together?

Data analysts and data engineers may work together, as they have complementary roles. Data analysts depend upon the data engineers to create the data architecture. Typically, companies have a team comprised of data engineers, data analysts and data scientists who handle, and manage all data-related issues.