Data engineers are in demand — 4 things to know before you pursue the field

The latest AI developments put data at the forefront since the quality of the data sets determines the quality of AI models. In other words, the rush for AI advancements is increasing the need for more data engineering jobs.

Every organization has tons of unstructured data that hide valuable insights. One of those insights can quickly boost profits, reduce operating costs, or improve well-being. The issue is that it’s buried under a lot of noise. Your job as a data engineer is to remove the noise by structuring and processing large amounts of data.

But before you enter this industry, consider these four things.

1. If you don’t enjoy diving into businesses, you might not like data engineering

If you enjoy writing code but don’t enjoy the business side as much, you might find data engineering both exciting and disappointing.

It will be exciting because you’ll need to build large systems at scale, but before you can write any code, you must understand the organization’s business context and its products.

You need to partner very closely with product managers and business analysts. Sometimes, you must partner with many to understand all business units. Even if the company has multiple units, the data system you build should serve all of them.

Before writing a line of code, you must figure out:

• What data needs to be collected?
• How is the organization planning to use the data?
• How fresh should the data be?

All of these questions have technical, architectural, and efficiency implications. A good data engineer delivers the best system that balances the organization’s needs, costs, and timelines.

The best data engineers consider future needs and designs with them in mind.

2. It’s a fast-paced job that continuously evolves

If you don’t like to be challenged constantly, then data engineering may not be for you.

Data engineering is a field that evolves very fast, and you need to keep up with it. Data is growing exponentially, and new systems and techniques are needed to manage vast amounts of data. Yesterday’s systems are obsolete, and this may seem overwhelming due to the speed of change.

It happened to me in the early stages of my career. There was so much to learn and so little time. I was working at a startup and had to hit the ground running. Early on, I read and learned at night and developed during the day. This helped me get a better understanding of software engineering than my peers.

Now, I set aside a couple of hours a week to browse and read about the latest developments in my field. I know I don’t cover everything, but I try to stay updated as much as possible.

The latest trends in the AI space have brought Generative AI and technologies like Vector Databases and Retrieval-Augmented Generation techniques. These unlock new possibilities but also bring new challenges.

You won’t be able to keep up with everything, but this shouldn’t make you give up. Register for tech newsletters. You can get a weekly or monthly digest of the new trends in your area. Be open to experimenting and trying new things. This will give you and your organization a competitive edge.

3. Knowing compliance rules is a must

As a data engineer, you must consider data privacy and build internal systems that satisfy legal and compliance requirements without putting the company at risk. You must familiarize yourself with GDPR, CCPA, HIPPA, and other regulations the business may need to follow.

I remember when the GDPR deadline kicked in, in 2018. There was a rush for all companies to stop what they were doing and become compliant. Engineering teams worked around the clock to make their systems compliant. The most considerable burden was on the data and feature teams to build in the corresponding functionalities.

Ensuring that user data is secure and that all processing and policies are transparent to users is crucial for building and maintaining customer trust. The easiest way to lose customers’ trust is not to manage their data well.

Data engineers must partner with legal and product managers to ensure all data flows respect data privacy regulations and update those flows when regulations change.

4. Consumers won’t see your work

You’re most likely mistaken if you think data engineering involves building consumer-facing features.

The products that data engineers work most of the time on are data platform systems and capabilities. Those systems support the organization’s business needs. In most companies, the data team is a horizontal team that builds systems and pipelines to structure and process the data.

The nature of the horizontal work involves data engineers having various internal customers, ranging from other software teams to product managers, analysts, and executives, who need valuable information on how to run the business.

Data engineers will only work on features and functionality indirectly for consumers. Still, their actions will influence where the product is headed by providing analytics and insights into the market and how customers use it.

Data engineering is an exciting field that is rapidly evolving

The rapid evolution of data engineering is key to making better AI models. But before jumping into the field, keep in mind the four points that were presented. This way, you will have your expectations set, and you will know what you signed up for.

Data engineering is a job that will allow you to interact with various stakeholders, from engineering teams to product managers, legal, and executives. It’s a foundational role for any modern organization.

If you are a data engineer with an interesting story to share about the job, please email Manseen Logan at mlogan@businessinsider.com.