Undoubtedly, one of the most crucial cogs in the digital machine is that of the data architect. While all data-related jobs play roles in our modern economy, the data architect is arguably the most indispensable.
That’s because they design, build, and maintain the infrastructure that supports all other data-driven business activities. And that’s pretty important!
But what is a data architect, exactly? What are their typical responsibilities, and how much can they earn? How might you go about becoming one? While the journey from novice data analyst to fully-fledged data architect won’t occur overnight, this introductory guide provides everything you need to get started.
We’ll cover all the basics, including:
- What is a data architect?
- Data architect tasks and responsibilities
- Types of data architects
- How much can I earn as a data architect?
- How to become a data architect
- Summary
Ready to learn more about this exciting and sought-after career? Then let’s get down to it.
1. What is a data architect?
A data architect is a specialist data professional responsible for designing the systems and processes that store and use data.
Their role can involve designing many types of data structures, from pipelines and databases to warehouses and cloud-based systems, to name a few. Broadly speaking, though, they aim to ensure that data are organized, accessible, secure, and up-to-date. They must also oversee the systems they create to ensure they are appropriately implemented and maintained.
When it comes down to it, data architects aren’t that dissimilar from real architects. Both consider how structures are used, while also addressing security and engineering concerns. In essence, data architects make sure that the different elements of a system fit together coherently, creating a solution that meets the varying needs of all its end users.
Why are data architects important?
While all data professionals are necessary for the success of a data-driven business, data architects are particularly essential.
Their work provides the foundation for all other data professionals: from data analysts and scientists, as well as business intelligence experts. Without a well-designed system, the work of these other professionals would be significantly hindered. And this is what makes data architects so unique.
2. Data architect tasks and responsibilities
Just like physical-world architects, a data architect’s tasks and responsibilities vary greatly depending on their role. However, since they typically lie at the intersection of data management and business, typical tasks might include:
- Designing the logical and physical structures of data systems, such as data warehouses and other data stores
- Creating the data models that dictate how data should be organized, stored, accessed, and maintained
- Understanding the available data sources and matching these to a business’s strategy and objectives
- In-depth knowledge of existing data security, data governance, and data quality standards, as well as the development of their data standards, policies, and procedures
- Ability to work with and integrate new technologies into an overall data architecture, e.g. cloud computing, data lakes, and artificial intelligence
- Ensuring that data is secure and meets all privacy and compliance standards.
- Troubleshooting any data-related issues and guiding others to optimize the architecture
- Communicating with key stakeholders to ensure they understand the data architecture and can use it effectively
- Managing the development and maintenance of data dictionaries and glossaries to help support non-technical system users
- Working with data engineers to ensure data pipelines, ETL processes, and other data-related processes continue to evolve and run smoothly
- Taking part in high-level business strategy, and translating an organization’s practical objectives into actionable tasks and systems design
As data systems grow ever more complex, the role of data architect is also evolving. Let’s explore how the role is changing in the next section.
3. Types of data architects
When data architecture emerged in the late 1950s and early 1960s, computer scientists had a (relatively speaking!) simple objective. For the first time, they had to organize, access and manipulate data in terms of logical models. This was a relatively novel concept back then, and the pioneers of that age laid the foundations for modern data structures.
While all these issues are still vital aspects of modern data architecture, the role has evolved since the 1950s. Today’s data architects can turn their hands to many complex tasks. But just as real architects have skills suited to different jobs, data architects now have increasingly specialist expertise, too.
As the use of data in business changes over time, niche data architecture roles have emerged. It’s impossible to predict where data architecture will lead in the future, but here are just a few types of data architects you might come across today:
- Database architects focus on the design, development, and maintenance of databases, and associated data-access technologies such as NoSQL clusters, SQL, and others.
- Data warehouse architects may specialize in larger-scale data structures. They are responsible for designing and implementing systems that store and manage multiple consolidated data sources.
- Business intelligence architects focus on the design of systems that facilitate the extraction of insights. This requires an understanding of the underlying data sources, an excellent grasp of data analytics, an organization’s strategic objectives, reporting requirements, and the technology used to present results.
- Enterprise architects are responsible for the oversight of an entire organization’s data architecture. They typically take a high-level view and will be involved in developing data-related strategies and ensuring that data architecture is rolled out in accordance with an organization’s goals and objectives.
- Big data architects focus on systems explicitly used to capture, store, process, and analyze large, complex, and typically unstructured data. Their work often involves distributed computing technologies like Hadoop, Spark, and Kafka. It may require additional skills such as distributed systems design and software engineering.
- Cloud architects are responsible for data solutions that run in cloud-based environments. This requires an understanding of cloud technologies like AWS, Azure, or GCP, as well as the ability to design solutions that consider the limitations of these technologies.
- Security architects specialize in creating security for specific systems or services, maintaining security documentation, and developing things like encryption and authentication. They’re also responsible for ensuring that data systems comply with relevant regulations.
- Machine learning architects (although not entirely new) are growing in popularity as AI plays an increasing role in our economy. They are responsible for developing and implementing machine learning models and algorithms and selecting the appropriate technology for these tasks.
In addition to these specific job titles, knowledge of data architecture and best practice are highly valued in other roles. Some data architects may apply their expertise to project management, sales, software engineering, and even c-suite executive positions.
In short, data architecture is a skill that will ensure you stay in demand, wherever your career might take you.
4. How much can I earn as a data architect?
By 2032, the number of data architects in the U.S. is projected to grow by 8%, according to the U.S. Bureau of Labor Statistics. For context, that’s comfortably above the average for all other occupations, at just 3%.
This growth reflects the demand for well-qualified data architects. It follows that you can earn a comfortable living in this role.
To get an idea of how much you can earn as a data architect in different global locations, we pulled data from Salary Expert’s website, whose insights rely on 30 years of real-world job data from the Economic Research Institute.
Here are five of the top-paying countries in the world:
- Switzerland: $131,423 (or 123,793 CHF)
- United States: $117,625
- Australia: $103,003 (or AUD 152,614)
- Germany: $98,387 (or €92,512)
- Canada: $92,908 (or $125,888 CAD)
Of course, these are just estimates. Other factors will impact your earning potential, too, including your experience level experience and the industry in which you choose to work. But, in general, data architects will earn significantly more than the average annual salary for other, less highly skilled jobs.
Get some in-depth insights into data architect salaries in this post.
5. How to become a data architect
Okay, so you’ve got the lowdown, and you’re convinced. Presuming you’re interested in data analytics (and have a little experience), how might you become a qualified data architect?
1. Develop your technical and analytical skills
Data architecture requires a deep understanding of different technologies and software systems. For starters, learn the basics of data modeling, data warehousing, and database design.
Additionally, you’ll need to be able to use various programming languages, from SQL to Python, Java, and more. You’ll also need an excellent working knowledge of the tools and techniques used to build and maintain data structures, for example, ETL (Extract, Transform, and Load) tools.
Don’t feel overwhelmed, though! While there’s plenty to learn, there’s also no rush to develop these skills. Play the long game and grab opportunities to learn new skills.
Next steps: Check out some YouTube tutorials on data architecture, or read the basics of creating a data architecture diagram (one example of a specialist topic).
2. Gain some relevant experience
While nobody is stopping you from working towards a career as a data architect, you’ll realistically need a few years of experience in more junior data analytics roles first.
As you climb the career ladder, actively seek job opportunities that will hone your existing skills and help you perfect new ones (for example, machine learning or cloud computing).
In general, familiarizing yourself with different data architecture types and how to use them in different contexts will widen your skillset and help you begin to understand where you might like to specialize.
Next steps: Check out jobs on websites like datajobs.com, aijobs.net, and analyticsjobs.co.uk. Also, browse the careers pages of tech-driven organizations in your local area.
3. Learn how to lead
While data analysis is a role where it’s often easy to work alone, that’s not the case for data architecture. Data architecture is about more than just technical skills, too—you need to be able to lead and manage a team of professionals.
This means knowing how to set goals, manage timelines, and delegate tasks. You should be able to communicate your ideas to colleagues and listen to theirs effectively. These are all vital leadership skills. Once again, you won’t learn them overnight, but you can start to improve them as you climb the ladder.
Take action: Get started by checking out some leadership programs. Initially, a free tutorial should be sufficient to offer you a taster. If you pay for a more in-depth program, keep your eyes peeled for topics like communication, coaching, accountability, negotiation, and change management.
4. Get qualified
You may already have a data analytics certification under your belt. If so, great! This is a brilliant way of demonstrating your knowledge and expertise to employers. However, as you progress, you might find adding additional certifications to your list beneficial, honing in on specific topics relevant to data architecture, such as data warehousing or database design.
If you’re super enthusiastic, another way to stand out from the crowd is to pursue a formal master’s in a subject like data science. While you don’t need to commit to this approach right away, remember that for some data architecture roles, a higher level qualification is a prerequisite for applying. It’s not always the case, but it’s worth keeping in mind.
Next steps: Explore this global list of data-related master’s degrees. While you don’t need to apply for one right now, it never hurts to see what’s out there! If nothing else, check out their program curriculums to find topics you might want to learn more about in your own time.
5. Network and build relationships
While it’s an in-demand role, data architecture is still a highly competitive discipline. Networking is a vital way of building relationships with other professionals working in the field.
Luckily, this is one step you can immediately start, regardless of your current skill level. Connect with existing colleagues, attend industry events and conferences, or join online groups and forums to meet fellow enthusiasts and senior experts. You may be surprised about the free advice and support people are willing to provide.
Next steps: Not ready to attend conferences in the real world just yet? Explore online workshops and seminars on sites like Eventbrite, or check out meetups for low-key networking events for data architects in your local area.
6. Stay up to date
Finally, keep your finger on the pulse! The field of data science is constantly evolving and expanding. It’s necessary to stay up to date with the latest trends, technologies, and emerging techniques related to data architecture and design.
Get into the habit of reading industry publications, attending seminars, listening to podcasts, and speaking to other professionals in the sector. If you start doing this now, it will become an organic part of your ongoing development.
Next steps: Get started by listening to The Hard Parts of Data Architecture podcast, or find another that suits your needs. There are a surprising amount of resources out there!
By following these steps and building a portfolio of your work over time, you’ll gradually develop the skills and experience you need to become a successful data architect. From here, all we can do is wish you the best of luck!
6. Summary
So there you have it, a comprehensive guide to a career as a data architect! In this article, we’ve learned that:
- Data architects are responsible for designing the systems and processes to collect, store, and use data effectively.
- The role of the data architect is highly varied. As the use of data in business evolves, many niche data architecture roles are emerging.
- Data architects can earn a great deal of money; top-paying countries include Switzerland, the United States, Australia, Germany, and Canada.
- To become a data architect, you’ll need to develop your technical and analytical skills, gain relevant experience, learn how to lead, network with other professionals, stay up to date on the latest trends and consider obtaining a higher-level qualification.
We hope you’ve found this job guide useful and are now inspired to start your journey to becoming a data architect.
If you want to learn about other potential careers in data analytics, check out this free 5-day data analytics short course. Alternatively, you can read the following introductory guides: