Posted Mar 21, 2022

In Data Science, General, Performance Management


Read time 5 mins

What’s Driving the Demand for Data Engineers?

The value of data has become so widely recognized, it likely won’t be long before it’s listed as an asset on a company’s financials. There’s no shortage of data to mine and analyze. According to, the volume of data/information created/captured, copied, and consumed worldwide from 2020 to 2025 is forecasted to increase from 64.2 to 181 zettabytes. However, the ability of companies to use that data effectively is largely reliant on sound data engineering.

A problem with having so much data is that it can be complex to model. Understanding and interpreting the meaning of data is critical to ensure that models aren’t fed bad or irrelevant data. AI (artificial intelligence), machine learning, and algorithms tend to dominate discussions of maximizing data value, but it’s essential to consider how that data is strategically collected, transformed, and distributed. That’s where good data engineering is essential. For companies to benefit from the data they’ve invested in collecting and storing, data engineers must ensure that the information presented to business users is meaningful, high quality, and trustworthy.

This article will look at what a data engineer does, the difference between a data engineer and a data scientist, what applicants can do to find the right fit as a data engineer, and what companies can do to attract and retain data engineers.

What does a data engineer do? defines data engineers as “vital members of any enterprise data analytics team, responsible for managing, optimizing, overseeing and monitoring data retrieval, storage, and distribution throughout the organization…. Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise.”

Data engineers capture and process raw data and move it efficiently through pipelines to reach the appropriate end-users. Those end-users could be data scientists and people using self-serve analytics. Specific projects such as cloud migrations would also rely heavily on the skills of a data engineer more than a data scientist.

Data engineers are also responsible for building models, removing bias from models, and maintaining the integrity of the data. Data isn’t static; data engineers will ensure that models are updated as it changes. For example, if a company traditionally sold products through dealers, and then added a direct-to-customer channel, what is a customer? Is it a dealer, the end-user, the customer that bought directly? Data engineers ensure that these questions are asked and answered to maintain data integrity.

What’s the difference between a data scientist and a data engineer?

The role of a data engineer has evolved, and some confusion lingers over the difference between a data engineer and a data scientist. Data scientists and data engineers work together; however, the data scientist’s role is typically further along the process, heavily involved in analytics. The data engineer on the other hand, will be positioned between IT and the data scientist, ensuring that the information that the data scientists have to work with is accurate. According to the Trends from the Burtch Works 2021 Salary Report on Data Engineers, data engineers will typically have a master’s degree or bachelor’s degree in Computer Science or Information Systems. Data scientists commonly have a Ph.D., along with Computer Science or Engineering degrees, and many have math/statistics or natural sciences backgrounds.

While not all companies will have both data scientists and data engineers, the value of data engineering tasks needs to be recognized, and those tasks need to be done by someone with the right skillset.

Companies are competing to hire the best data engineers with solid technical skills and the ability to translate business user needs into technical requirements.

What applicants can do to find the best fit as a data engineer

The demand and salaries for data engineers are prompting people with data warehousing, query-writing, and ETL experience to use the Data Engineer title and apply for these jobs. However, it’s essential to realize that data engineering is now a cloud-focused role that requires open source knowledge, Python skills, using libraries that are someone else’s work, and comfort with a distributed computing environment. Someone interested in being a data engineer could benefit from having Azure, AWS, and change management skills.

What companies can do to attract and retain data engineers

LinkedIn included Data Engineer as an emerging job in its 2020 Emerging Jobs Report, which revealed that the hiring growth rate of professionals in this job has increased by nearly 35% since 2015. You’ll find more than 75,000 data engineers on LinkedIn, but the demand exceeds the supply.

There are some red flags that may dissuade a data engineer from joining a team. One is a lack of data governance. It’s too much to expect a data engineer to shoulder the full responsibility of ensuring that regulatory, compliance and security requirements are defined, met, and enforced. Data engineers enjoy designing and implementing a structure, but they don’t want to be in endless janitorial detail, re-cleaning the same data over and over. Also, if a data engineer role encompasses what a data engineer and data scientist should be doing, that could be a signal of a lack of structure that would dissuade applicants.

Data engineers are essential contributors to the success of analytics initiatives. If you’re looking to build an analytics team, Newcomp can help you create an infrastructure and find the right data engineers using techniques that outperform hiring off resume credentials. Contact us to learn more.

About Newcomp Analytics

Newcomp Analytics is a world-class analytics team specialized in consulting and development at the highest level. Wherever you are in your analytics journey, we will help you raise your game in today’s complex business environment. Our team of data scientists, engineers and developers deploy analytics for hundreds of clients across all industries and functions around the globe. We have a simple formula to help you win with data: define the goal, find the gaps, then match you with the right people, processes, and tools to get there – coaching you the entire way. For more information, visit or email [email protected].

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