Data architect vs data engineer: the key differences
By Indeed Editorial Team
Published 9 November 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Companies produce significant amounts of data through daily processes, from creating reports to inputting customer information. Both data engineers and data architects provide critical services in managing, processing and quantifying data, which businesses require for important decisions, in-depth analysis and transparent reporting. If you're considering a career in data, roles in data architecture or data engineering are options that continue to grow and evolve as global business requirements change. In this article, we define what data architecture and engineering is and explain the key differences in data architect vs data engineer jobs.
Data architect vs data engineer: What are they?
Before explain the differences between data architect vs data engineer, first we could explain what each role entails:
A data architect is a trained professional responsible for the formulation of data strategies within organisations. Their role in data management is to define the different principles and standards a business uses to operate, creating a 'data blueprint' that implements different business practices and implementations. Technical expertise in the design, creation, management and deployment of data systems is a requirement for this role, with a strong understanding of business scenarios and solutions necessary to achieve goals. Key responsibilities for data architects include:
conceptualising and visualising data framework at an enterprise level
using extraction transformation and load tools and spreadsheets to gain knowledge
creating roadmaps for data management design, integration and centralisation
machine learning visualisation and design
designing data warehousing solutions
A data engineer is an expert responsible for the maintenance, optimisation and designing of infrastructure necessary for data access, collection, management or transformation. This role requires trained professionals to use their expertise to convert raw, unprocessed data into usable formats for analysis, reporting and testing. Data engineers consistently ensure data is secure, available and accessible to relevant internal specialists, implementing data policies and collaborating with internal teams to leverage existing data effectively. Key responsibilities for data engineers include:
building and maintaining architecture and data frameworks
gathering and processing raw data
using software engineering to manage systems
maintaining databases and pipelines
implementation of machine learning frameworks
designing and creating data applications
Differences in data architect vs data engineer roles
When considering these roles, it's important to consider the functionality and responsibilities of each role. For example, while architects and engineers use data in their typical duties, the specification of each job covers different skill sets and areas of expertise. Some of the key differences between data engineers and data architects include:
The core responsibilities of data architect and data engineer roles differ. For example, a data architect has involvement in the high-level visualisation and decision-making process to determine how to store data. A data engineer is more involved in technical responsibilities to implement these plans and ideas, using their skills and experiences in programming and databases to manage specific tasks. While both roles deal with data, the functionality of the position differs in how that knowledge helps businesses.
Data engineers directly work on technical aspects of data science and software engineering. For many responsibilities of the role, a strong understanding of SQL and NoSQL database systems is a necessary standard to create, manage and maintain various data storage applications. Knowledge of programming languages is also integral for this role, plus understanding the technical process for data wrangling, integration and migration within the workplace.
The skills necessary for data architect roles have less emphasis on technical capabilities. Understanding how to model, integrate and manage data from a top-level perspective is a crucial skill that is helpful in data architect roles. As data architects often work with upper management or high-level professionals, strong soft skills in leadership and communication are also important. Data architects may have a history of working in data science or engineering but use their knowledge for guidance instead of applying their skills to daily data management.
The strategic focus of data architect roles is on providing businesses with advice and guidance to manage their data and put it to good use effectively. A data engineer focuses on putting this advice into operation, using their knowledge and skill to apply changes, implement solutions and manage the transition from raw data into a valid format. Data engineers may work directly for data architects, aligning their focus with the theoretical goals of the data architect to achieve results.
Data engineers use their skills and technical knowledge to work on the data that flows into a business directly. Their hands-on role involves managing, converting, implementing and adjusting data flows, requiring excellent programming and database systems knowledge. Data architects may have technical ability, but their involvement in standard operations is less common. For example, a data architect with data engineering experience may provide their opinion on a particular process, which engineers implement within the company.
Data engineers use their knowledge for practical applications, such as ensuring consistent data accessibility or managing problems as they arise. Data architects use their insight and years of experience to share their knowledge with businesses as internal advisors or external consultants. Architects often have a history in data science and engineering, with supplementary knowledge gained through training or experience to advise on architecture, governance and modelling.
A data architect may be capable of conducting specific data engineer tasks, but a data engineer doesn't have the knowledge or expertise to provide data architect services. As a role that requires extensive experience and insight into large-scale data systems, data architects are high-value team members that dictate data handling processes. Data engineers use their technical knowledge to design and create these systems to an architect's specifications.
The typical responsibilities data architects and engineers handle also impact what their working day looks like. For example, a data engineer may spend more time working in front of a computer alone or as part of a team, utilising blueprints to implement new data solutions and maintaining existing systems. Data architects may spend more of their time providing expert advice and researching new technologies and data solutions, requiring more time away from a desk and regular communication with upper management.
Data architects' project involvement in implementing new solutions is typically in the early stages. For example, a data architect may produce the blueprints and guidance engineers use to implement systems, requiring their input at the start of the project for decision-making purposes and visualising the full scope of the solution. Data engineers are directly involved in the middle and later stages of projects, using their expertise to put theory into practice by adjusting, designing and testing various data solutions.
Data engineers have extensive experience managing data and database systems, utilising practical knowledge gained through training or responsibilities to complete necessary tasks. Data architects are typically highly experienced professionals who may have a background in data engineering and an understanding of different systems and processes from other perspectives. Many data architects start in other data-related roles, gaining practical experience to gain the knowledge necessary to visualise data acquisitions and designs.
Completing a degree or diploma in data science is a standard option for data engineers, with practical training in specific programming, database languages and functionalities to fulfil the job's responsibilities. Data architects may continue their training to a master's or research doctorate level, gaining additional experience and theoretical knowledge. The exact training for a data architect depends on their past experience and understanding of data science, with some architects moving into the role without returning to education.
Determining which job suits you
Data science is a complex and expanding field, with roles changing and adapting as the business uses for data grows. For example, companies focusing on machine learning and artificial intelligence to acquire and manage data have more demand for skilled data architects and data scientists to fulfil specific management, analysis and warehousing requirements. Many organisations offer certifications in particular areas of data engineering and architecture, providing the opportunity to learn and develop your skills.
Your ideal career path depends on your skills, experience and preferences. For example, if you're considering an entry-level role in data, becoming a data engineer allows you to work directly with different databases and systems. If you already have experience in the data science field, finding a job as a data architect may suit you if you prefer to move away from direct involvement in building and maintaining frameworks and architecture.
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