Data Analyst. Data Wrangler. Data Architect? If you like pulling together threads of a company’s Data into one cohesive point, you may want to consider a Data Architect role.
But what exactly is a Data Architect and how does it differ from a Data Engineer?
Data Architect vs. Data Engineer
As businesses continue to combine their Data and business strategies into one, they are beginning to understand to the need for a variety of Data Analysts. But as important as it is to have someone build your platform and begin pipeline processes, there is also need for someone with vision. Someone who can see patterns and designs. Someone who has end-to-end vision and can see how the patterns flow through your processes. This is your Data Architect.
Data Engineers, on the other hand, lay the foundation for your Data platform. They draft the blueprint. After all, you can’t build a house without a blueprint first, right? The Data Engineer is at the beginning of the process, so the rest of the team can do their parts. But it’s the Data Architect who pulls it all together.
THE ROLE OF THE DATA ARCHITECT
If you’re considering your next career move and wondering if Data Architecture is for you, here are some typical requirements. A typical Data Architect will:
- Meet with stakeholders to understand business needs and translate them into technical requirements using ETL techniques to develop Data Architecture
- Understand their full Data lifecycle to provide technical architecture leadership
- Design a real-time data pipeline ecosystem and how to make it scalable using
- Develop Big Data Architecture in an AWS environment
- Be educated to a degree level in a numerate discipline (Mathematics, Statistics, Computer Science, Computer Engineering)• Have proven experience in a commercial environment
- Have advanced Cloud Computing Ecosystem experience with AWS (GCP or Azure also considered)
- Have proven Big Data Ecosystem experience
- Have proven Big Data Architecture experience in a commercial environment
- Have proven Data Engineering experience in a commercial environment
Though the likes of Google, IBM, and others have ramped up their education efforts, and online courses traditional universities offer a variety of Data Science degrees, there is still a shortage of professionals in the industry. So can businesses simplify and automate processes without the right people in place?
Businesses Step Up Their Data Strategies
Though there are easier ways to get the information a business needs through rented predictive modelling or an already drafted Data Science model, it doesn’t give the true value of Data. Add in new regulations, requirements, and new Data which offer new insights, and the impact on business is profound.
It’s time for business to start ensuring that their Data teams are treated as critically as possible. Time to lay a path of progression, a pipeline, of systems and processes for the
creation and production of Data. After all, simply optimising your Data will only get you so far. Enterprise-wide Data systems are more than
wrangling and analysing Data.
Most importantly, businesses need to ensure they have the right people in place. They also need to understand what they need and why they need it. This is a key part of Data Strategy and with the right people in place, can put your business ahead of the competition.
Digging Deeper into Requirements for Top Talent
While the standard requirements for a Data professional are to be educated to a degree level in things like Computer Science and Mathematics, technical skills, and experience within certain industries, for the natural progression from Data Analyst to Data Architect, there’s a bit more nuance to consider.
Whether your business is just getting started in Data Science or you’re ready to start growing an existing team, there are some things you may want to focus on when looking for your Data Architect role.
Define and determine how to keep projects streamlined with repeatable processes.
Pivot between guiding team members through the pipeline and explaining insights to executives and stakeholders.
Determine the right format for the right project.
Determine when and when not to use automation to integrate Data.
Visualise and extract models to predict future events and describe the process. In other words, be able to interpret Data to ensure reliability of the best approach.
With the right talent in place, your teams can collaborate and build on their shared expertise to ensure Data is analysed and understood to the best benefit of your business.
If you like solving puzzles, pulling disparate threads together into organised systems, and have experience as analysing and collecting Data, we may have a role for you.
Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.
