Decoding Data Roles: An Insight into the Difference between Data Architects and Data Engineers

Two major roles play a crucial part in an organisation’s data strategy – the Data Architect and the Data Engineer. Whilst these titles both have different purposes within data management, the titles are often confused, and responsibilities can be overlapped. The collaborative responsibilities contribute to the overall success of a data-driven enterprise.

For both Architects & Engineers to work together, communication is vital as Nigel Shaw (DSFederal) mentioned in my post from last week, the “Data Architect thinks, the Data Engineer does.” An organisation needs to have people within their data team that are behind the design and structure of the data ecosystems. We see responsibilities that include strategic planning with business goals, high-level designs, and data modeling.

Data Engineers are known for turning this blueprint into a reality and are involved in the implementation and maintenance of the data infrastructure. Some of the responsibilities include database management and using different programming languages to create solutions for data processing and storage.

Constant adaptation is needed due to the changing needs of an organisation, and this can sometimes come with it challenges. Aaron Wilkerson (Carhartt) mentioned that “if you have multiple projects in motion, the data architect can only do so much work. Then you have the Data Engineer step into help do some of the data architect tasks.”

This is why we see so many Data Engineers move along in their career into the architecture space. As the DA role includes stakeholder engagement, good communication and problem-solving, these are skills that Engineers can pick up along the way. As well as someone that can keep up to date with the latest trends in the industry, be opinionated on why certain technologies should be used and understand why it would be beneficial for an organisation.

Both roles push organisations towards innovation and growth and will always be quite pivotal however we do see this with other positions across the data landscape. Gabriele Fusta (Velux) highlighted that he see’s “many Data Scientists doing Data Analyst work but being paid as a DS. This distorts the market and creates confusion.” As many organisations are creating hybrid roles that combine different elements of traditional positions, this can blur the lines and make it difficult for Data Leaders and their internal teams to hire.

Morgan Templar (First CDO Partners) explained that “getting clear on roles, titles and responsibilities is something that we (data leaders) should work on better alignment and definition.” Clear job descriptions and transparency on the responsibilities will create a resourceful data team and a collaborative culture within a business.

No doubt, that we will see the overlapping of different roles in the near future and maybe new roles arise like the “Analytics Engineer” and the “Data Scientist.” Data Leaders need to be prepared for this to happen as new technologies evolve and clear on their expectations from the get-go.

Natalia is a highly skilled talent partner. With a passion for identifying top talent and a strong commitment to customer service, she has established herself as a valuable resource to both clients and candidates in the data & analytics industry. Read more.