Big Data Architects and Big Data Engineers are roles which fall under the Big Data management heading as they both help pull together a company’s data into one cohesive point. But what are the exact responsibilities of a Big Data Architect and a Big Data Engineer, and how do they differ from one another?
The role of a Big Data Architect
Many businesses are at the start of their data management journey – trying to pull their business and data strategies together – and so they are also just beginning to work out what types of Big Data analysts they need. Now, while it is really useful to have people in your business who can build your data platform and set up your data pipeline process, you also need someone who has the vision to see how data patterns and designs may flow through your process. This person with vision is called a Big Data Architect.
If you are wondering about how to become a Big Data Architect, then the typical day to day tasks will include:
- Regular communication with stakeholders to understand the business needs
- Translating business needs into technical requirements to help develop the Big Data architecture using ETL (Extract, Transform and Load) techniques
- Understanding of the full data lifecycle to provide technical architecture leadership
- Design a real-time Big Data pipeline and ensure it is scalable
- Develop Big Data architecture in an AWS (Amazon Web Services) environment
If you are considering data architecture as a career, then the most common educational and skill requirements include:
- Degree level education in a numerate discipline e.g. maths, statistics, computer science or computer engineering
- Advanced cloud computing ecosystem experience with AWS (Amazon Web Services), Azure (Microsoft) or GCP (Google Cloud Platform)
- Proven experience in a commercial environment
- Proven experience of a Big Data ecosystem
- Proven experience of Big Data architecture in a commercial environment
- Proven experience of data engineering in a commercial environment
The role of a Data Engineer
So if the Big Data Architect is the visionary who sees how data will flow through your business through the use of a Big Data platform, then the Big Data Engineer is the person who lays the foundation for that vision. The Big Data Engineer is the essential person at the start of the process who enables the rest of the data management team to play their parts. They are experts in the design, build and maintenance of data-based systems and organisational operations.
Big Data Engineers are tasked with laying the foundation for data to be captured, analysed and translated into something that business leaders can understand – and they have to do this in a timely manner. After all, timely data capture not only leads to more data but it also allows business leaders to come up with better predictions.
The role of a Big Data Engineer also includes working closely with Big Data scientists to ensure they can deploy the code and models correctly. In fact, some companies would go so far as to say that the role of a Big Data Engineer is more important to them than the Big Data Scientist role.
Now, if you want to become a Big Data Engineer, the route is not as straightforward as other data roles. In this case, experience really does mean more to employers than education, which perhaps explains why there are no real Big Data courses on offer yet. Data Engineering is such a specific, focused role that it is hard to teach. However, you still need a framework to build on. The best way to break into data engineering is to:
- Gain a bachelor’s degree in computer science, applied maths, physics, statistics or software or computer engineering
- Build up your data skills by gaining certification in the main computer languages, such as Java, Python and Scala
- Take courses in data engineering technology, like AWS, Azure, GCP, Hadoop and Spark
- Join a professional organisation of Data Engineers such as The Data Warehousing Institute (TDWI) or the Institution of Engineering and Technology (IET) – a great resource for advice and articles
- Apply for a data science fellowship with Faculty (an AI company) as this gives you the opportunity to complete an 8 to 10-week intensive project with one of their partner companies, helping them to solve real-world business problems using data engineering or data science skills
As you can see there are specific differences between the role of Data Architect and Data Engineer, but both are vital for any business which wants to be a success. Having both these roles within your company will ensure you are not just optimising your data – but also have a pipeline in place to create and produce it in line with your data strategy.
If you want to make sure you have the right talent in place or are looking to become a Data Architect or Data Engineer, then please get in touch with one of our expert consultants to find out more.