[HR Hiring Guide] Data Engineer vs Data Scientist vs Data Analyst

This blog has been inspired from an interview with Technical.ly Media CEO Chris Wink on ‘How to Build a Data Team.’ You can read the full article or watch the interview with Data Engineer Instructor Nicholas Dela Fuente and Chris here.

The fastest-growing team in your company just might be the data team, so we’re going to explore how to build an effective team with the right roles in place.

To start, the three pillars of most data teams are data scientists, data engineers and data analysts.

But let’s dive deep into these terms to have a robust understanding about what they mean starting with a brief overview of how these roles work together.

  • Data engineers are fundamental in terms of getting data to the point in which it can be analyzed.
  • Then, data scientists help make sense of the data by building models.
  • Finally, data analysts employ some type of interpretation of the various collections of data that guide business insights.


But let’s look at these roles a bit closer to understand these professionals, so you can hire the most effective role for your team.

Data Engineer: The Overlooked Foundational Role

If you don’t have your data in a collected, organized form, then hiring a data scientist, isn’t going to be as efficient as much as hiring data engineering. Simply put, a data engineer is somebody that deals with the architecture of data at your company. Moving data around can be expensive, especially if you’re dealing with large amounts of data; engineers know how to move data in an efficient cost-effective manner.

data science code with womanOne of the first things you should do is hire a data engineer because these data engineers are going to implement, test and maintain the infrastructural components that are fundamental to the actual data team itself.

Data science is a quickly growing field and data engineering has been growing just as fast in the background, but it hasn’t gotten nearly as much publicity as data science.

Because data scientist is a recognizable title by most people in an organization, it’s usually the advertised job opening in the company. This issue is a branding problem.

This means, it’s not uncommon for a company to hire a data scientist and expect them to perform data engineering tasks. They want the new hire to understand the different types of data storage or know whether to not to use a relation or a nonrelational database.

A data scientist has the ability to understand those concepts, but a data engineer is somebody who’s specialized in the methods for moving data and building the data architecture. They lay the foundation for data scientists to start using the data.

I find that many companies are hiring data scientists but are really looking for a data engineer in disguise.

As a result, a bunch of these data scientists are getting hired, but they have to learn all of these new skills that aren’t necessarily what a data scientist should be doing. Ultimately, it was a data engineering job that they got hired for with a little bit of data science on the side.

There is a saying that data science is mostly data cleaning; it consumes more than 80 percent of the work hours. That’s even more true when you don’t have a data engineer.

It can be a bit frustrating for a company and for somebody getting hired as a data scientist and not having the supporting infrastructure that allows them to do their work.

Data Scientists: Build Complex Models 

The data scientist understands the models. They know how to capture inferences and insights from the data to build systems

In addition, they can piece together an array of technical tricks to create sophisticated models that squeeze out the last drop of performance.

Their value lies in leveraging their technical virtuosity over millions of situations where even small gains aggregated across millions of users and trillions of events can lead to massive wins.

Data Analysts: Communicate Effectively to Stakeholders

A data analyst is going to have business knowledge. These data professionals make statistical inferences about the data and present it to the people who drive the decisions in the business.

Data analysts might have a background in marketing, sales or some other field they’ve mingled with, but most importantly, they know how to make business sense of the data scientist’s work.

The ideal data analyst is an expert communicator. They are agile in that they can interact with the marketing team, data scientists and leaders within the company because they have a broad understanding of these two separate worlds that depend heavily on each other.

Chief Data Officer or Chief Analytics Officer: A Leader Who Prioritizes Time Investments

Data engineers like to carefully build systems for data collection and cleaning. Data scientists want to strategically build and run models. But, data scientists can’t start until data engineers finish, which means there is a natural conflict that arises between these two roles.

This is why I think it’s really essential to have a chief analytics officer or some type of chief data officer. This person is responsible for directing the communication between these two fields.

The ideal hire for this role will have knowledge of the field, bridge the gap between data science and data engineering and maybe have some additional domain expertise.

You want this person to be the visionary and technical lead who’s adept at guiding the data teams in the right direction.

Questions to Ask Before Hiring Data Professionals

If you’re a startup company, you might not have much data, but you could in the future. Many companies, startup or not, are moving in the direction of trying to do something with their data because data is going to be a fundamental part of most businesses.

It is never going to be a bad idea to at least consider moving forward with hiring a data team, because they do provide valuable insights to the marketers, to the product team, to the supply team, the finance team, and to the business in general.

However, before you start hiring your data team, there are several questions you should ask:

  1. What are you trying to accomplish with this data?
  2. Do you even have data?
  3. Who needs the data?
  4. Do we have data that is important?
  5. Do we have big or small data?
  6. Are you trying to get business insights?
  7. Are they going to provide better analytics for our marketers?
  8. Are they going to be at the forefront of the decisions we make in the business?
Data Employees eBook 21.07.1 Page 01

Are you looking for more insights for hiring data professions? Download a copy of our latest ebook Find & Hire the Right Data Employees.

And, explore the benefits of becoming a TDI Hiring Partner. At The Data Incubator, we train the best data scientists and data engineers available today – with the latest tools and technology they can apply to your data from day one – and ensure they’re ready to use their skills to enhance businesses like yours.

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