��ɫ��Ƶ

While data architects* and database architects are often mistaken for each other, they are different roles. Both are crucial to handling a company’s data, but their focus, responsibilities and associated industries differ.

Data architects take a bird’s-eye view of an organization’s needs and develop comprehensive data strategies. Their broad scope is concerned with how data flows through the entire organization, from initial collection to final analytics. Once final analytics are completed, data modelers then implement these strategies and solutions.

They typically work in technology companies, healthcare organizations, financial firms and government agencies. They often work closely with C-suite executives, business analysts, data scientists and other similar roles.

Database architects typically home in on the design and implementation of specific databases. Their specialty lies in database technology, and they often focus on resolving complex technical issues. They generally handle:

• Designing the structure of databases, including tables, relationships and indexes
• Fine-tuning databases for optimal performance
• Implementing security measures at the database level
• Handling data migration and database upgrades

Database architects often work with e-commerce platforms, companies with legacy systems needing database modernization, organizations with specialized database needs, or firms in retail, logistics or supply chain management, where database performance is critical. They’re usually part of a technical team and may report to IT managers, data architects or other technology leadership roles.

While these two positions may work closely together, they handle equally important but different aspects of data. However, those aspiring to pursue either role may share comparable educational backgrounds.

*��ɫ��Ƶ does not educationally prepare students to become a data architect; however, it does help prepare for roles as a database architect. Through the Bachelor of Science in Data Science program, students learn core skills such as data analytics, data management, data visualization and business intelligence.

As with most jobs, a data architect’s daily duties may vary. So, depending on the employer, industry or specific project, the following responsibilities will change, but the general functions remain the same.

:

• Turning high-level data model and management policies into specific applications, database models and more.
• Working with team leaders and managers. They often have a large hand in application design because of the correlation between business requirements, data and application.
• Working with CIOs and their staff. They need to communicate line organizational data collection and usage requirements. They work to align requirements with database and application technologies.

These are foundational to the role and provide valuable guidance for anyone considering this career path.

The nature of this job is based on technical information, so education is critical. A  is generally the minimum requirement to be considered for this role. For senior positions, employers may look for a master’s degree in a related field.

Professionals who practice data architecture must possess both the technical prowess to build robust data frameworks and the soft skills to support the organizational needs that those systems serve.

When it comes to technical skills,  are expected to be proficient in various technologies and methodologies. Given the increasing reliance on data across virtually all industries, there is demand for the following skills:

• IT skills: A strong foundation in information technology is essential, as these professionals often work closely with IT departments.
• Database management systems: Expertise in technologies like SQL, Oracle or MongoDB.
• Data modeling and warehousing: How to design data models and manage data warehouses.
• Programming languages: Proficiency in languages such as Python, Java or R.
• Applied math and statistics: These professionals must have the ability to use math skills.

Non-technical or “soft” skills are equally important. They help bridge the tech-focused and business-focused sides of an organization. These include skills like:

• Communication: These data professoinals must effectively communicate complex technical concepts to non-technical stakeholders.
• Teamwork: They often represent data science and data architecture in working with cross-functional teams. Being a good team player is essential.
• Leadership: In shaping an organization’s data strategy, data architects need leadership skills to garner buy-in across departments.

���ٳ����� for this role include analytical and problem-solving abilities, which help to effectively communicate the insights of data engineers in a collaborative, multidisciplinary environment. Data engineers will then manage and convert raw data into information that is usable and accessible. Data engineers develop, test and maintain data structures that the data sciences ten use for analysis.

The ubiquity of data means that this role is not confined to any one industry; computer science has a broad spectrum of job opportunities. However, there are a few :

• Finance and insurance
• Scientific and technical consulting
• Management of companies and enterprises
• Computer systems design and related services

These data professionals also work in healthcare, retail, logistics, government and many other fields. If working in any of these industries interests you, you can seek out specific job availabilities within them by researching companies and checking out their career pages.