Data Architect: Role Description, Skills, Certifications and When to Hire
Data is now one of the most valuable assets for any kind of business. The 11th annual survey of Chief Data Officers (CDOs) and Chief Data and Analytics Officers reveals 82 percent of organizations are planning to increase their investments in data modernization in 2023. What’s more, investing in data products, as well as in AI and machine learning was clearly indicated as a priority.
This suggests that today there are many companies that face the need to make their data easily accessible, cleaned up, and regularly updated. Hiring a well-skilled data architect can be very helpful for that purpose.
In the article, we explore the role of a data architect, discuss the responsibilities and required skills, and discusses what kind of companies may need such a specialist.
What is a data architect?
A data architect is an IT professional responsible for the design, implementation, and maintenance of the data infrastructure inside an organization. This specialist works closely with people on both the business and IT sides of a company to understand the current needs of the stakeholders and help them unlock the full potential of data.
To get a better understanding of a data architect’s role, let’s clear up what data architecture is.
Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. It serves as a foundation for the entire data management strategy and consists of multiple components including
- data pipelines;
- on-premises and cloud storage facilities –data lakes, data warehouses, data hubs;
- data streaming and Big Data analytics solutions (Hadoop, Spark, Kafka, etc.);
- machine learning and deep learning models; and
- business intelligence tools.
If you are not familiar with the above-mentioned concepts, we suggest you follow the links above to learn more about each of them in our blog posts.
Bad data management be like, Source: Make a Meme
Data architects are sometimes confused with other roles inside the data science team. Let’s discuss and compare them to avoid misconceptions.
Data architect and other data science roles compared
Data architect vs data engineer
A data engineer is an IT specialist that develops, tests, and maintains data pipelines to bring together data from various sources and make it available for data scientists and other specialists.
What is the main difference between a data architect and a data engineer? In short: Data engineers technically implement the vision of the data infrastructure, designed by data architects. However, in practice, many companies don’t necessarily have data architects so there are only data engineers and this distinction won’t be applicable.
The daily tasks of a data architect require greater strategic thinking, while a data engineer’s workload is more about building the software infrastructure, which is comprised of technical tasks.
By the way, we have a video dedicated to data engineering working principles. Feel free to enjoy it.
Look behind the scenes of the data engineering process
Data architect vs data analyst
A data analyst is a specialist that makes sense of information provided by a data engineer and finds answers to the questions a business is concerned with.
On a daily basis, data analysts investigate data and report results using data visualization tools to marketing and product management teams, C-level executives, and others.
Therefore, the roles of a data analyst and a data architect are fundamentally different. The former uses data to generate insights and help businesses make better decisions, while the latter designs data frameworks, flows, standards, and policies that facilitate effective data analysis.
As we have distinguished a data architect from other roles in a data science team, let’s proceed to more detailed information on a data architect’s daily tasks and duties.
Data architect responsibilities
Data architects are highly-qualified, senior-level specialists with multiple responsibilities that will differ depending on the business needs of the organization. However, we can identify some key tasks that are the same for most companies. Let’s discuss each of them in detail.
Translating business requirements into an effective data infrastructure
Data architects collect and document business requirements to clearly define objectives a company wants to reach with data. They also inspect existing sources of information and where it resides inside the company. Another important task is to evaluate the company’s hardware and software and identify if there is a need to replace old components and migrate data to a new system.
Eventually, data architects create a blueprint — or a high-level schematic — of data infrastructure, build data flow diagrams, and offer a tech stack that will support the data management strategy and make data bring business value.
Sample of a high-level data architecture blueprint for Azure BI programs. Source: Pragmatic Works
This specialist also oversees the deployment of the proposed framework as well as data migration and data integration processes.
Monitoring and optimizing the data infrastructure
Data architects have to monitor and maintain the system health by completing regular tests, troubleshooting, and responding to problems in a timely manner. They also define KPIs to measure and track the performance of the entire data infrastructure and its separate components. If KPI goals are not met, a data architect recommends solutions (including new technologies) to improve the existing framework.
Data architects are not that threatening, but inspiration is always welcomed, Source: Dev79
Ensuring data security and compliance with regulations
A data architect decides how data is protected and who has access to it. Besides, it’s up to this specialist to guarantee compliance with laws, regulations, and standards related to data. Let’s take the example of healthcare data which contains sensitive details called protected health information (PHI) and falls under the HIPAA regulations.
If a company works with medical datasets and documents outside healthcare facilities, it’s up to a data architect to take care of setting access restrictions, encryption, anonymization, and other security measures.
In the EU, the General Data Protection Regulation (GDPR) sets guidelines for collecting, storing, and processing personal information. This privacy law must be kept in mind when building data architecture.
Setting a data governance policy
A data governance policy is a document that covers data management goals, procedures, and business expectations. It defines metrics and best practices to ensure data quality as well as data privacy and security. The document keeps everyone on the same page about who is responsible for what and how data must be managed at different stages of its lifecycle. Not the only person who creates a policy, a data architect contributes a lot to establishing rules and standards around data.
The way it works for any IT role, a data architect has to possess a set of hard and soft skills to become a first-rate professional.
Data architect’s hard & soft skills
Overall, a data architect is a senior-level technical role that requires a wide range of professional knowledge, abilities, and competencies. So when hiring one, it’s crucial to make sure the candidate has the hard and soft skills necessary to fulfill all above-mentioned responsibilities. Let’s start from the hard skills and discuss what kind of technical expertise is a must for a data architect.
Proficiency in programming languages
Even though in most cases data architects don’t have to code themselves, proficiency in several popular programming languages is a must. This includes
- Java, widely used for data engineering and across Big Data technologies (a large part of the Hadoop ecosystem is written in Java);
- Python as one of the most popular languages in data science; (basic programming language frequently used for data projects and popular among data engineers and data scientist);
- SQL, standard query language used for accessing and manipulating data in relational databases; and
- Scala, a language of Apache Spark, Kafka, and some other Big Data tools.
Hands-on experience with a wide range of data-related technologies
The daily tasks and duties of a data architect include close coordination with data engineers and data scientists. This specialist supervises data engineers’ work and thus, must be closely familiar with a wide range of data-related technologies like SQL/NoSQL databases, ETL/ELT tools, and so on.
To effectively communicate with data scientists, data architects have to understand the key data science concepts such as data modeling, data analysis, ML framework,etc.
Apart from this, a huge benefit for a data architect is the knowledge of popular analytics techniques as well as common tools, such as Microsoft Power BI and Tableau.
This skill is necessary to investigate data entities a company produces, explore relationships and dependencies between them, and define the best ways to organize data for further use. It also involves creating a visual representation of data assets. To perform or supervise data modeling, data architects must have expertise at database administration and SQL development. Besides, proficiency with widespread modeling tools like Enterprise Architect, Erwin, or PowerDesign is mandatory.
Metadata management skills
Metadata management unlocks the value of a company’s data and it’s a data architect’s task to ensure metadata principles are applicable to all data a business has. This means a data architect should have a good grasp on the data lifecycle management (DLM) and understand the way metadata is used during each step of DLM.
Communication and other soft skills
As a mediator between business operations and its technical implementation, a data architect is involved in multiple interactions and must have well-developed soft skills. Here are the main ones.
Communication skills. To better understand what data the business requires and how it should be collected and managed, data architects communicate with C-level managers of the company and other stakeholders.
On one hand, data scientists have to communicate those business requirements to technical teams. On the other hand, they must clearly articulate the current limits and capacities of the organization’s data infrastructure to managers, so that both parties (business and IT specialists) can equally contribute to the overall objectives.
Multitasking. This skill helps data architects manage multiple projects simultaneously, and prioritize their workload. The ability to interleave multiple tasks and responsibilities saves a lot of time for data architects and improves their overall performance.
Problem-solving skills. They refer to the ability of a data architect to troubleshoot multiple complex issues with data systems, timely identify the source of problems, and come up with effective solutions to them.
Business mindset and critical thinking. As previously mentioned, data architects act as interpreters between businesses and data science specialists. Their mission is to align business needs with technical specifications. To succeed with this complex task, they must be business-oriented. clearly see a company’s objectives, and leverage their technical expertise to minimize expenses and maximize profits.
Data architecture is not the IT sphere one can enter from scratch. In the next section, we discuss what kind of education and work background is required to become a data architect.
Data architect career path
To become a data architect one should obtain a bachelor’s or a master’s degree in computer science, data science, information technologies, or related fields. However, the relevant educational background is not the only requirement. An applicant for this senior-level position must have at least seven to eight years of proven experience in software engineering, data engineering, database administration or other data-related specialty.
The table below represents the skillset, educational background, and work experience of data architects that are now working for the data-driven enterprises we all know.
Background and skills of data architects. Examples from LinkedIn
Data architect certifications
To confirm its skills, a data architect can get the appropriate certifications. At present, there are very few certifications designed specifically for data architects, yet data engineering and data analytics certifications are applicable as well.
We have selected for you the information upon some relevant certifications offered by the leading professional education platforms. For sure, this list is not exhaustive, and you can find other relevant options online.
Arcitura Certified Big Data Architect
Exam duration: 170 minutes
Exam price: $249 USD
Languages available: N/A
Arcitura Certified Big Data Architect is a certification for data architects, data engineers, and other seasoned data professionals . It consists of five modules: Fundamental Big Data, Fundamental Big Data Architecture, Advanced Big Data Architecture, Big Data Analysis & Technology Concepts, and Big Data Architecture Lab.
The certification confirms the ability to design and implement big data solutions inside the enterprise or within the cloud-based environments. You can pass the exam passed worldwide via Pearson VUE Online Proctoring. But first, all candidates must be accredited by Arcitura as Big Data professionals. The Big Data Science Certified Professional (BDSCP) program includes 15 course modules and exams covering such topics as Big Data analysis, engineering, architecture, governance, and more.
AWS Certified Data Analytics
Exam duration: 180 minutes
Exam price: $300 USD
Languages available: English, Japanese, Korean, Simplified Chinese
AWS Certified Data Analytics is meant for specialists who have at least five years of proven expertise with common data analytics technologies and two years of experience with AWS services to build data analytics solutions. They also must understand the main principles of how these services are implemented in data collection, storage and data visualization.
The exam is delivered through the AWS testing center network and is typically proctored in person. In some locations, this certification can be acquired online.
Microsoft Certified: Azure Data Engineer Associate
Exam duration: 150 minutes
Exam price: $165 USD
Languages available: English, Chinese (Simplified), Japanese, Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian, Indonesian (Indonesia).
Microsoft Certified: Azure Data Engineer Associate covers the knowledge of Azure data services, data security in the cloud, and data management.
The candidates for this certification should be able to transform, integrate and consolidate both structured and unstructured data. Also, they must have in-depth knowledge of data processing languages like Python, Scala, or SQL. A deep understanding of data architecture patterns and how parallel processing works is required as well.
The exam is delivered via the Microsoft testing center network and is typically proctored in person. There are two ways to prepare for the exam: first is self-paced preparation, and another one is picking up an instructor-led course.
Cloudera Data Platform Generalist Certification
Duration: 90 minutes
Price: $300 USD
Languages available: N/A
Cloudera Data Platform Generalist Certification (CDP Generalist) is a certification that confirms general broad knowledge of Cloudera’s CDP platform. It can be applicable for multiple roles such as data analyst, data architect, data engineer etc. To pass the exam, a candidate should possess knowledge of the key CDP components — HDFS, Ozone, Hive, YARN, Spark, HBase, Oozie, Kafka, and other data technologies.
When companies needs to hire a data architect
Not all companies need a data architect. For instance, small businesses can’t afford a data architect (whose services are usually quite pricey) as a full-time employee. Hiring this expert or training the one internally is recommended in the following cases.
You run a large data-driven business. If your company is data-oriented and you need to build a robust data infrastructure, a data architect is worth considering. This specialist will modernize an existing data infrastructure and implement data policies and procedures that will fit your unique requirements. With a robust data management system in place, you may count on practical insights from your existing data and informed decision making.
Your business needs optimization of the existing databases. A data architect can optimize your databases or make the relevant recommendations about the suitable database design. This specialist defines and monitors the way databases are formed and maintained. With the help of a data architecture, businesses can identify when and how it’s better to move data from the existing databases to new ones.
You have problems with data management inside the company. Data architect suggests what kind of technologies must be used in the organization so that the company’s data is securely passed from the source of storage to its endpoint. A data architect can help companies design and implement data infrastructure that is secure, scalable and can be optimized for the current business needs.