Data architects visualize and design an organization’s enterprise data management framework, aligned with enterprise strategy and business architecture.
Data architect role
Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles, often in support of data or digital transformations. The data architect is responsible for visualizing and designing an organization’s enterprise data management framework. This framework describes the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data.
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 and related business architecture,” according to DAMA International’s Data Management Body of Knowledge.
Data architects are frequently part of a data science team and tasked with leading data system projects. They often report to data infrastructure and data science leads.
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Data architect responsibilities
According to Panoply, typical data architect responsibilities include:
Translating business requirements into technical specifications, including data streams, integrations, transformations, databases, and data warehouses
Defining the data architecture framework, standards, and principles, including modeling, metadata, security, reference data such as product codes and client categories, and master data such as clients, vendors, materials, and employees
Defining reference architecture, which is a pattern others can follow to create and improve data systems
Defining data flows, i.e., which parts of the organization generate data, which require data to function, how data flows are managed, and how data changes in transition
Collaborating and coordinating with multiple departments, stakeholders, partners, and external vendors
What are different types of data architect?
Data architecture is a complex and varied field and different organizations and industries have unique needs when it comes to their data architects. Data architect Armando Vázquez identifies eight common types of data architects:
Enterprise data architect: These data architects oversee an organization’s overall data architecture, defining data architecture strategy and designing and implementing architectures.
Solutions data architect: These individuals design and implement data solutions for specific business needs, including data warehouses, data marts, and data lakes.
Application data architect: The application data architect designs and implements data models for specific software applications.
Information/data governance architect: These individuals establish and enforce data governance policies and procedures.
Analytics/data science architect: These data architects design and implement data architecture supporting advanced analytics and data science applications, including machine learning and artificial intelligence.
Cloud data architect: The cloud data architect designs and implements data architecture for cloud-based platforms such as AWS, Azure, and Google Cloud Platform.
Data security architect: The data security architect works closely with security teams and IT teams to design data security architectures.
Big data architect: The big data architect designs and implements data architectures supporting the storage, processing, and analysis of large volumes of data.
Data architect vs. data engineer
The data architect and data engineer roles are closely related. In some ways, the data architect is an advanced data engineer. Data architects and data engineers work together to visualize and build the enterprise data management framework. The data architect is responsible for visualizing the “blueprint” of the complete framework that data engineers then build. According to Dataversity, data architects visualize, design, and prepare data in a framework that can be used by data scientists, data engineers, or data analysts. Data engineers assist data architects in building the working framework for data search and retrieval.
Data architect vs. data scientist
According to Dataversity, the data architect and data scientist roles are related, but data architects focus on translating business requirements into technology requirements, defining data standards and principles, and building the model-development frameworks for data scientists to use. Data scientists are experts in applying computer science, mathematics, and statistics to building models.
How to become a data architect
Data architect is an evolving role and there is no industry-standard certification or training program for data architects. Typically, data architects learn on the job as data engineers, data scientists, or solutions architects and work their way to data architect with years of experience in data design, data management, and data storage work.
What to look for in a data architect
Most data architects hold degrees in information technology, computer science, computer engineering, or related fields. According to Dataversity, good data architects have a solid understanding of the cloud, databases, and the applications and programs used by those databases. They understand data modeling, including conceptualization and database optimization, and demonstrate a commitment to continuing education.
Data architects have the ability to:
Design models of data processing that implement the intended business model
Develop diagrams representing key data entities and their relationships
Generate a list of components needed to build the designed system
Communicate clearly, simply, and effectively
What are the daily duties of a data architect?
According to builtin, the day-to-day responsibilities of data architects include:
Using data modeling tools such as ER/Studio to visualize and design data architecture
Using programming languages like Python to code, test, and troubleshoot data architecture applications
Documenting data pipeline procedures or queries for metadata and reference data cases
Communicating with data analysts and database administrators to implement procedures and determine infrastructure needs
Data architect skills
Data architects require math and computer science proficiency, data management skills, and the ability to analyze and present statistical information.
According to Bob Lambert, analytics delivery lead at Anthem and former director of CapTech Consulting, important data architect skills include:
A foundation in systems development: Data architects must understand the system development life cycle, project management approaches, and requirements, design, and test techniques.
Data modeling and design: This is the core skill of the data architect and the most requested skill in data architect job descriptions, according to Lambert, who notes that this often includes SQL development and database administration.
Established and emerging data technologies: Data architects need to understand established data management and reporting technologies, and have some knowledge of columnar and NoSQL databases, predictive analytics, data visualization, and unstructured data.
Communication and political savvy: Data architects need people skills. They must be articulate, persuasive, and good salespeople, Lambert says, and they must conceive and portray the big data picture to others.
Data architect certifications
While there are no industry-standard certifications for data architects, there are some certifications that may help data architects in their careers. In addition to certifications in the primary data platforms used by their organization, the following certifications are popular:
Certified Data Management Professional (CDMP)
Arcitura Certified Big Data Architect
IBM Certified Solution Architect – Cloud Pak for Data v4.x
Salesforce Certified Data Architect
TOGAF 9 Certification Program
For more information about these certs, see “Top 8 data engineer and data architect certifications.”
Data architect salary
According to compensation analysis from PayScale, the median data architect salary is $131,027 per year, with total pay, including bonuses and profit share, ranging from $83,000 to $172,000 annually.
Here are some other popular job titles related to data architecture and the average salary for each position, according to PayScale:
BI architect: $83K-$149K
Data engineer: $68K-$135K
Data warehouse architect: $78K-$154K
Database architect: $83K-$172K
Information architect: $71K-$164K
Solutions architect: $80K-$170K
Data architect jobs
A recent search for data architect jobs on Indeed.com showed positions available in a range of industries, including consulting, financial services, healthcare, higher education, hospitality, logistics, pharmaceuticals, retail, and technology.
A sampling of data architect job descriptions shows key areas of responsibility such as: creating a DataOps and BI transformation roadmap, developing and sustaining a data strategy, implementing and optimizing physical database design, and designing and implementing data migration and integration processes.
Companies are looking for bachelor’s degrees in computer science, information science, engineering, or equivalent fields, though master’s degrees are preferred. Most are looking for 8 to 15 years of experience in a related role. They want highly motivated, experienced innovators with excellent interpersonal skills, strong collaboration, and the ability to communicate effectively verbally and in writing.
Are data architects in demand?
Data architects are in strong demand. The US Bureau of Labor Statistics says there were 149,300 data architect jobs in the US in 2022 and projects the number of data architects will grow by 8% from 2022 to 2032. That’s faster than average for all other occupations in the US. Demand for data architects is especially high in organizations with 1,000 or more employees.
More on data architecture and science:
What is data architecture? A framework for managing data
The top 8 data engineer and data architect certifications
Essential skills and traits of elite data scientists
Developing data science skills in-house: Real-world lessons
The age of the citizen data scientist has arrived
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