In a relatively short span of time, the COVID-19 crisis has completely transformed the way companies in all sectors do business. A new survey by McKinsey Global finds that COVID-19 has accelerated the adoption of digital technologies by several years—and that many of these changes could be here to stay. During the pandemic, consumers have made a dramatic move toward online channels, and in turn, companies and industries have responded. As a result, this has created more demand for data-related roles in organizations across industries, some of which have been more affected than others.
According to the Monster Annual Trends report, 96% of the companies are planning or are likely to plan to hire new staff with relevant skills to fill future big data analytics roles in 2022. A data architect is one of the key people involved in building and supporting an organization's big data needs.
In this blog, you will learn more about who a data architect is, and more specifically, what a data architect does. We will also look at how the role is different from that of a data engineer, and what skills you need to become a data architect in 2022.
What is a Data Architect?
As reported by the Data Management Body of Knowledge, a data architect establishes a common business vocabulary, articulates strategy-driven data requirements, maps out advanced integrated designs to align with these requirements, and ensures that enterprise strategy and the related business architecture are in alignment with one another.
While the actual role and responsibilities of a data architect may differ slightly by company, a general definition of the role can be given as someone in an organization with a senior position playing an important part in technical data. They translate business requirements into technical requirements and define standards and frameworks through which data is collected, stored, retrieved, archived, and transferred across enterprise applications. The data architect also “provides a standard common business vocabulary, expresses strategic requirements, outlines high-level integrated designs to meet those requirements, and aligns with enterprise strategy,” according to DAMA International’s Data Management Body of Knowledge.
Open Group Architecture Framework (TOGAF) defines a data architect as a position expected to set data architecture principles, create models of data that enable the implementation of the intended business architecture, build diagrams depicting core data entities, and create an inventory of the data necessary to make the architecture vision a reality.
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What does a Data Architect do?
Data architects have a wide range of organizational duties and they collaborate with many other roles and departments within an organization, including:
- Domain experts: Data architects will often work with domain experts and business owners directly. They play a key role in application design as they convert the business requirements into technical specifications.
- Chief information/technology officer: Data architects work closely with leadership in defining the data strategy and communicating it to the entire organization.
- Other data-related roles: Data engineers, database developers and specialists, database administrators, and software engineering teams.
The exact roles and responsibilities of a data architect position may differ by company, location, and size of organization, but a generalized look at roles and responsibilities of a data architect position could look something like this :
- Translate business requirements into technical specifications.
- Define and design the integrations, databases, and data warehouses.
- Define the data architecture framework, standards, and principles–including the security framework.
- Define data flows (i.e., which parts of the organization generate data, which require data to function, how data flows are managed, etc).
- Aim for continuous improvement in an organization’s data architecture.
- Collaborate with a wide range of technical and non-technical stakeholders and external partners and vendors.
- Collaborate with leadership and senior management to devise and execute data strategy to meet organizational goals and objectives.
- Maintain a corporate repository of all data architecture blueprints and artifacts.
- Constantly strive for improvements in scalability, security, performance, data recovery, reliability, etc.
Data Architect vs. Data Engineer
The roles of a data architect and a data engineer are related but hold two different technical positions in a data organization, both of which are extremely important.
Design and envision data architecture
Execute the vision and develop the architecture to specifications
Focus on leadership and high-level data strategy
Focus on the day-to-day tasks of data cleaning, data wrangling, and preparing data for other data consumers in the organization such as data scientists, data analysts, etc.
Data architects typically have practical skills in a large number of data management tools, including data warehousing, data management, data modeling, and various ETL tools.
Data engineers are usually required to have expertise in relational and non-relational databases, ETL, automation, big data tools, cloud, and production-level coding skills.
Data architects are responsible for the conceptualization and visualization of data frameworks.
Data engineers work on building and maintaining those frameworks.
Entry-level roles are not likely.
Entry-level roles are possible.
One key difference is the seniority level. While data engineering roles are available at entry-level, data architect roles are mostly available as senior positions requiring more than 8 years of experience. Seasoned data architects come from many tracks; however, data engineering is the most common. Data science is another entry point for data architect positions.
Skills of a Data Architect
Technical Skills for Data Architects
- Relational and non-relational databases
- Data warehousing
- Application server software (e.g. Oracle)
- Database management system software (e.g. Microsoft SQL Server)
- User interface and query software (e.g. IBM DB2)
- Enterprise application integration software (e.g. XML)
- Agile methodologies
- Data modeling tools (e.g. ERWin, Enterprise Architect and Visio)
- ETL tools
- Python, C/C++ Java, Perl
- Cloud (e.g. Azure, AWS, GCP)\
Other non-technical skills that are quite important in a data architect role
- Communication skills to facilitate collaboration with other departments
- Analytical and problem-solving skills to protect data integrity, organization, and security
- Time management and the ability to multitask so that you can accomplish tasks and complete projects in a fast-paced environment
- Program or project management skills – typically in regards to managing change within a business, as well as project management methods and tools, etc.
- Business skills and methods – usually comprising business cases, business process, strategic planning, etc.
- Other soft skills – generally including leadership, teamwork skills, presentation skills, interpersonal skills, etc.
Salary of a Data Architect
According to Glassdoor, data architects have an average base pay of around $129,000 USD annually. Advancement into EA and management roles could increase their salaries to $200,000 USD per year or more. On average, data architects earn $25,000 in annual bonuses and other income.
How to Become a Data Architect
A data architect is not a regulated profession so having a degree is not a set requirement. However, it is very common for data architects to hold an undergraduate degree in computer science, information technology, software engineering, or any other related discipline. Self-taught data architects are not uncommon, but it takes a lot more time and discipline to achieve the same skill level. There are ample certifications available in this field that you should also consider along the way for career advancement and growth.
Get a university degree
The first step to becoming a data architect is to get a degree in one of the following majors: data science, computer science, information technology, or software engineering. Taking classes on database management, data architecture, software design, or computer programming can be extremely beneficial in a data engineering career.
Get professional certifications
There are countless industry certifications available for those who want to get into the data engineering field, such as:
Certified Data Management Professional (CDMP)
Developed by the Data Management Association International (DAMA), the CDMP is a common certification on data architects’ résumés. Since it does not focus on a particular platform or vendor, it serves as a solid credential for general database professionals. There are four levels (associate, practitioner, master and fellow) to be awarded to candidates who show proof of the necessary experience and education as well as passing results on CDMP’s professional exam.
DataCamp Data Engineer with Python Career Track
In this comprehensive career track, you’ll learn how to build an effective data architecture, how to streamline the processing of data, and how to maintain large-scale data systems. In addition to sharpening your Python skills, you’ll get hands-on experience with additional languages like Shell, SQL, and Scala as you master data engineering pipeline creation, common file system task automation, and high-performance database construction.
IBM Certified Data Architect – Big Data
This certification program requires prerequisite skills including cluster management, replicating data, data lineage, and LDAP security. The final exam focuses on Hadoop, BigSQL, BigInsights, and Cloudant.
Salesforce Certified Data Architecture and Management Designer
Designed for candidates with experience in working with the Salesforce platform, the data architecture and management designer certification exam tests your understanding of large data volume risks and mitigation strategies, LDV challenges, managing an LDV environment, and design trade-offs to name a few.
TOGAF® 9 Certification Program
The TOGAF Professional Certification has two parts: foundation and then certification. The basis of this credential is to verify that candidates have demonstrated knowledge of the terms and essential concepts of TOGAF 9 and the core principles of TOGAF and business architecture.
You also have the option of choosing from the best data science bootcamps, which we explore in more detail in a separate article.
Learn programming languages
Other Data Related Careers
As you consider whether becoming a data architect is the right path for you, it might be helpful to examine it in comparison with other careers. To learn more about the other common data roles, check out the following blogs:
- How to Become Data Engineer
- How to Become a Machine Learning Engineer
- How to Become a Data Analyst
- How to Become a Data Scientist
The following chart offers a brief visual comparison of the other common data roles:
Data architecture is a rapidly-growing career niche: with the pace of digitalization increasing across industries due to COVID, the profession has developed exponentially in the last two years. Because it is constantly evolving, you will never feel bored on this career path. What’s more, it is highly competitive and pays extremely well.
While each of the technical skills required for a data architect cannot be taught in one single course, it is highly recommended to take the Data Engineering with Python course on Datacamp. This track will equip you with the foundational skills you’ll need to become a data architect, as well as career guidance upon completion.
Data Architect FAQ's
What is a data architect?
An important senior role in data organization, in which he/she translates business requirements into technological requirements, and defines standards and frameworks through which data is collected, stored, retrieved, archived, and transferred across enterprise applications.
Which programming languages should a data architect learn?
The most common programming languages for data architects are SQL, Spark, Hive, and Python.
What is the difference between a data architect and a data engineer?
Data architects design and envision enterprise data architecture while data Engineers execute the vision and develop the architecture as per specifications.
How do I become a data architect?
It is very common for data architects to hold an undergraduate degree in computer science, information technology, software engineering or any other related discipline. Self-taught data architects are not uncommon, but it takes a lot more time and discipline to achieve the same skill level. Online training programs, such as DataCamp's Data Engineer with Python Career Track, are excellent options to arm yourself with the foundational knowledge you'll need to be a successful data architect.
Do I need to go to university to become a data architect?
Absolutely not. A data architect is not a regulated profession, and hence a University degree is not mandatory (though sometimes required for jobs). Online training programs, such as DataCamp's Data Engineer with Python Career Track, are excellent options to arm yourself with the foundational knowledge you'll need to be a successful data architect.
How much does a data architect earn?
According to Glassdoor, data architects have an average annual base pay of around $129,000 USD. Data architects also earn $25,000 in annual bonuses and from other income.
What software does a data architect need to know how to use?
Python, SQL, Relational and non-relational databases, ETL, Cloud, C++, Java, Hadoop.
Is a data architect a good career?
Data architects are generally senior-level professionals who are highly valued in large companies and typically the highest paid of all the data roles; these factors make it an excellent career choice.
Is a data architect the same as a data engineer?
The data architect and data engineer titles are closely related and, as such, frequently confused. The difference in both roles lies in their primary responsibilities. It's important to note that while you can find entry-level positions as a data engineer, data architect positions require many years of experience.
Courses for Data Architects
Mastering API Design: Essential Strategies for Developing High-Performance APIs
Data Science in Finance: Unlocking New Potentials in Financial Markets
5 Common Data Science Challenges and Effective Solutions
The 12 Best Azure Certifications For 2024: Empower Your Data Science Career
A Data Science Roadmap for 2024