It is already obvious that the demand for data engineers, data architects, and data scientists is higher than ever. According to the U.S. Bureau of Labor Statistics (BLS), data scientist roles will continue to be among the rapidly growing jobs and is expected to continue rising in 2025. The projected increase in job openings and demands from 2022 to 2032 is 35%. As we continue to generate and use data to improve critical business decision-making, the demand for data professionals will continue to increase.
Furthermore, BLS states an 8% job growth for database administrators and data architects from 2022 to 2032. With demand for these services, Glassdoor reveals a notable rise in the average salaries from $153,000 in 2024, which is expected to rise in 2025.
The data engineer and data architect roles are often interchangeably used. Although there are some roles that overlap, they share some specific duties that help us understand how both roles operate within this field. In this article, you will learn about data architect vs. data engineer and how both roles are in demand. So, whether you are looking to enter this field or need to understand which service you need, this article will clear it up.
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What is a Data Engineer?
A data engineer is accountable for maintaining, designing, and optimizing data infrastructure for data management, transformation, collection, and access. Data engineers create pipelines that transform raw data into usable formats for data scientists and other data consumers to use effectively.
The role evolved around a data engineer is to handle the core data aspects of software engineering and data science. They use software engineering principles to develop algorithms that automate the data flow process. Data engineers also team up with data scientists to build machine learning and analytics infrastructure, from testing to deployment.
Key Responsibilities for Data Engineer
Here, we will cover the primary responsibilities of a data engineer. These include providing that the data is ready for viewing, secure, and accessible to stakeholders when needed.
Building and Maintaining Data Infrastructure
Building and maintaining data infrastructure for the optimal transformation, extraction, and loading of data from a wide variety of sources. These include such as Amazon Web Services and Google Cloud big data platforms.
Guaranteed Data Accessibility
It is critical for a data scientist and those who need their data to ensure that it is safe to access at all times. Implementing company data rules and policies regarding data privacy and confidentiality is one core responsibility of a data engineer. Maintaining data privacy is a critical stage for a data engineer since there is a possibility of bugs in the data. A data engineer is expected to deliver secure data that can be utilized easily.
Cleaning Data from Sources
It is critical to clean up and wrangle data from primary and secondary sources into formats that can be efficiently used by data scientists and other data consumers.
Excellent Collaboration
A mandatory responsibility for data engineers is to have excellent collaboration with engineering teams, data scientists and other stakeholders to understand how data can be effectively used to meet business requirements.
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What Skills Should a Data Engineer Have?
Data engineering is a combination of software engineering and data science. This means that knowledge within both fields of expertise is important and is regarded as a major advantage. Since data engineering heavily depends on programming, many of the data engineers start their careers as software engineers and then lean towards data engineering.
Here are a few prerequisite skills needed for data engineering:
- Database systems knowledge in SQL and NoSQL
- Data Migration and Integration
- Data Cleaning
- Data Processing
- Programming Language
- Cloud Computing
Although each organization’s roles and requirements are slightly different, you will notice similarities in skills and common themes, such as the ones outlined in the above list, throughout the job descriptions for data engineer roles. For instance, the person responsible for this data engineer infrastructure engineering role at TikTok will team up with data scientists and software engineers to build big data solutions.
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What is a Data Architect?
A data architect is responsible for formulating the organizational data strategy and defining the data management principles and standards on which the organization operates. Data architects design the “data blueprints” that other data consumers implement and follow.
They specialize in creating the organization’s logical and physical data assets and setting data policies on the basis of the company’s needs. Data architects are often industry veterans who have worked in many data roles, gained experience navigating complex business scenarios, and designed solutions that data teams can implement.
Key Responsibilities for a Data Architect
The data architects’ first and foremost responsibility revolves around providing deep technical expertise for designing, managing, creating, and deploying large-scale data systems in the organization. Here are a few highlighted responsibilities:
Design, Develop, Implement, and Translate
Data architects are required to design, develop, implement, and translate business needs and the overall organizational data strategy. This strategy includes the principles, standards, storage, pipelines, data sources, data flows, and data security policies.
Collaborations with Data Engineers and Scientists
When it comes to this field, teamwork and collaboration are essential. Collaboration with data scientists, engineers, and other stakeholders is mandatory to execute the data strategy effectively.
Communication and Definition
Defining and communicating the data architecture patterns in the organization that guide the data framework.
Leading Data Teams
Data architects have to lead data teams to develop scalable, high-performance, secure and reliable big data and analytics software services.
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What Skills Should a Data Architect Have?
Data architects need a combination of technical and soft skills to succeed. Typically, they start in other data roles, such as data analyst, data scientist, or data engineer, and work their way up to becoming data architects after years of experience with data modeling, data management, and data design.
Here are a couple of important skills:
- Data Modeling, Integration, Design, and Data Management
- Databases and Operating Systems
- Data Architecture
- Data Security
- Data Governance
- Communication and Leadership Skills
Although every organization has slightly different needs, you will notice similar skills and common themes, such as the ones mentioned above, throughout the job descriptions for this role. For instance, a data solutions architect with roles with Lightspeed asks that candidates have experience with data management and SaaS (Software as a Service) tools and build data solutions and models for the teams.
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Data Engineer vs. Data Architect: Know What the Differences Are
Now that we have covered the single definitions of a data architect vs. data engineer, we will compare them collectively, side-by-side, to understand better and see how closely related they are and how different they are from one another. The difference lies in both of the role’s primary responsibilities.
Here are some things to note:
- Data architects provide technical expertise and guide data teams on bringing business requirements to life. This happens when data engineers provide data which is readily available, accessible, secure, and organized to stakeholders (i.e. data analysts and data scientists) when they are in need of it.
- The data architect and data engineer work together hand in hand together to build the organization’s data system.
- Data architects design the blueprint and vision of the organization’s data framework, while the data engineer is accountable for creating that vision and turning it into life.
- Data architects have substantial experience in data integration, data modeling, and data design and are often experienced in other data roles. For example, data engineers have a strong foundation in programming with software and engineering experiences.
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Data Engineer vs. Data Architect: Which One to Choose?
When you decide to choose a career and hire a professional, note that the data architecture role requires years of experience in a previous data-related role; both roles need a deep understanding of database systems, data processing tools, and experience working with big data. In other words, an effective data management team is a must to understand the differences between the roles.
When you consider interviewing a data engineer for hire, it is important for you to know that they must have a solid understanding of various databases, data wrangling, organizing, and processing techniques. As for data architects, on the contrary, you must be sure to ask what data projects they have led in the past and get a clear sense of understanding of their data philosophy. It is important to remember that a data architect will be the leader of your data management team. And you should be confident about the one you choose.
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