Data engineers: here’s what your job will look like in the future
Data engineers are highly sought after. According to Dice (a technology job platform), data engineers represent the fourth most posted occupation, having increased 100% YoY.
When it comes to data engineers vs data scientists, we tend to make the following distinction: data engineers are those who build and maintain systems and structures that store, extract, and organize data, whereas data scientists are those who analyze the data to use for business insights and forecasting.
In the past, data engineers managed data warehouses, as well as massively parallel processing (MPP) databases. Then they started to use scalable analytics for business intelligence. Eventually, this role became known as ‘big data engineering’, but the title soon became obsolete, as data would keep growing to unimaginable scales.
Today, a data engineer has a much different role.
The current state of data engineering
A data engineer’s job today involves overseeing the entire data engineering process - from data collection at source to making the data ready for use by other business-critical initiatives or teams.
According to the recently published Fundamentals of Data Engineering, data engineering describes “the development, implementation, and maintenance of systems and processes that take in raw data and produce high-quality, consistent information that supports downstream use cases, such as analysis and machine learning. Data engineering intersects security, data management, DataOps, data architecture, orchestration, and software engineering.”
The role of the data engineer today, then, is to “manage the data engineering lifecycle, beginning with getting data from source systems and ending with serving data for use cases, such as analysis or machine learning.”
As a result of data engineers having to think about the entire data process, there has been a shift in focus away from simply constructing data pipelines to thinking of data as a product and considering its end use.
In other words, a data engineer needs to think about what the data is meant to deliver first (such as a dashboard to view particular analytics or opportunity gaps), then build a data pipeline based on those needs.
With this process in mind, data engineers are also now adopting the concept of ‘semantic layering’. By using best practices from software engineering, they can create a ‘translation’ layer that helps turn raw data into understandable business metrics. At Switchboard, we call this foundational data.
So how do these trends translate into how data engineering might look like in the future?
The future of data engineering
Research suggests the data engineering services market will have a CAGR of 16.3% between 2020 and 2026 - which means more growth, adaptation, and specialization.
With data engineers now responsible for so many processes already, their role, and also the supporting tech, will continue to evolve in the following ways:
The simplification of data tools. Having to manage more data and data processes than most people can imagine, the tools that engineers use will likely add more features and functionalities to help make the job easier. As a result, engineers will also likely need to use fewer tools, as existing tools develop and adapt.
Improved data management with DataOps. As data continues to grow and become more complex, teams will need to develop a DataOps mindset - which refers to the management method focused on optimizing communication, data flow automation, and integrations among data consumers and administrators.
A shrinking gap between data consumers and producers. As data becomes increasingly integral to multiple stakeholders within a business (such as the owner of a data source system and data pipeline builders), priorities will shift towards having closer, more established relationships between producers and consumers of data.
An increase in role specialization. As trends start gearing toward software engineering practices and product-led thinking, more specialized roles will likely increase in popularity such as data analytics engineers. In the future, “data reliability” engineers will likely emerge as companies also turn their attention to data quality and availability.
While the traditional role of the data engineer is evolving - with job titles and responsibilities morphing into more specialized roles - data engineering will always be needed.
As such, data engineers of the future will likely be responsible for consulting on the tools and data processes that will provide the best value to a business, as well as designing flexible, big-picture architectures.