Data Architects

Data Architects design data structures for customer data platforms, data marts, CRM systems and other systems of record. They put order to the chaos of conflicting data streams by orchestrating plans to integrate, centralize, protect and maintain their strategies for implementation. This detailed stratagem allows other team members and clients the time and space to access critical information, and understand the content as it applies to their needs.

Skills That Set Them Apart

Data Architects are specialists who can turn client needs into reality, with a near-equal skill-split between a technical computer background and problem solving techniques. Innovation plays a large role in Data Architects’ daily responsibilities – they invent ways to organize data and identify optimal data storage platforms to address client needs.

Other helpful qualities include:

• Effective Communication: Being able to understand, relate and collaborate with team members and clients to determine the problems at-hand and the viable solutions that work best within the parameters.
• Predictive Maintenance: Being able to respond to existing market trends while keeping a perspective on what-if scenarios for the future.
• Decisive Planning and Incremental Implementation: Big data can create a spaghetti-like tangle of ifs and thens. Data Architects can slice through the congestion to resolve issues before they impact results. 
• Impressive Industry Knowledge: Every industry can have its challenges, so understanding the specific challenges and functions of data collection, analysis and utilization can maximize data leveraging techniques.

The Educational Foundation That Sets the Stage

Data Architects typically begin with an undergraduate degree in Computer Science, Computer Engineering or Mathematics, with an emphasis on application design, systems development and information management. 

Data Architects are encouraged to pursue their master’s degree – specifically in concentrations that involve data management, programming and analytics – if they want to advance to more senior roles.  

It’s a growing field, one that continues to evolve and change. 

Post-graduate Data Architects continue their education in the form of various scripting and programming certifications, where they learn to hone their skills to their chosen industry. They may focus on application architecture, network management and performance management, while they use online resources to sharpen their programming abilities.

R Programming

While it’s difficult to learn, it’s worth it. Data Architects use R programming to solve statistical questions because it’s designed specifically to solve data science issues. Data Architects are also very likely to understand other data science tools and platforms such as NumPy or MatLab.

Python

Data Architects are often skilled in Python, as well as Java, Perl and C/C++. Python is relatively easy to learn, and it is supported by an active community. Python has been gaining on R in popularity in recent years, though both of these open-source languages are popular.

SQL

It’s the backbone of complex queries and universal among Data Architects to execute complex queries in SQL (structured query language). This platform consistently ranks at the top of the list of needed skills for Data Analysts and other data managers.

NoSQL and Columnar Databases

Data Architects should also be familiar with some NoSQL such as MongoDB or HBase and columnar databases such as Redshift or its competitors. These systems work quickly with large volumes of data and are easily scalable for a more customized approach. The traditional relational database, while a long-reigning requirement, is becoming less and less effective for the larger-scale data industries now face. NoSQL platforms are easier to scale and can create solutions more quickly.

Big Data Computation Networks 

Many Data Architects learn the Apache Hadoop platform, and knowing Hive or Pig just adds to their arsenal.

Apache Spark is also popular because it’s faster than Hadoop – a boon when running extremely complex algorithms.

Cloud Tools

In addition to platforms such as Hadoop, some Data Architects are also experienced in working with cloud-based tools such as Amazon S3. 

Artificial Intelligence

Data Architects may have a basic understanding of artificial intelligence and machine learning techniques.  Data Architects working in this area should be familiar with decision trees and logistic regression in order to solve problems and make statistical predictions, which allow businesses to make better decisions.

Comfortable with Data that Cannot Be Pigeon-Holed

Customer reviews, blog posts and other unstructured data doesn’t fit neatly into tables, so Data Architects find the best ways to store it and access it. This kind of information can add even more details to existing data, honing your decision-making process.

Certified Data Management Professional (CDMP)

The four levels – associate, practitioner, master and fellow – create a stair-step path to expertise in data management, and can help Data Analysts achieve industry-related credibility. The CDMP certificate applies to any data professional rather than on a specific specialty or platform. Instead, it’s a credential that is widely respected. The ongoing re-certification renewals mean that they’re always up-to-date on recent technology and applications. 

What do Data Architects typically earn per year? Take a look at what some of the job sites have to say:

Xperra Data Architects utilize the latest technology to provide real-time answers and solutions by designing the appropriate data structures for customer data platforms, data marts, CRM systems and other systems of record.

Xperra’s Data Architects are visionaries. They’re able to handle disparate data from a variety of sources, and conceptualize and visualize data frameworks. They also play a definitive role in the following responsibilities:

• Design and implement effective database solutions and models to store and retrieve company data.
• Identify database structural necessities by evaluating client operations, applications and programming.
• Assess database implementation procedures to ensure they comply with internal and external regulations.
• Prepare database design and architecture reports for management and executive teams.
• Recommend solutions to improve new and existing database systems.