Why has the Data Engineer Position Become so Popular Among Startups?

Data Engineer Position

According to a report, data engineering is one of the most popular jobs that were growing fast in 2019. Just then, it had a growth of 50% when it came to open positions. Many companies in the IT industries have noticed this growth. It has increased the interest in data engineering and made it more popular on the market.

It is safe to assume that data engineering jobs will grow and grow more in the future.

What Does a Data Engineer Do?

Data engineers are responsible for designing and building pipelines. These pipelines usually transform data – so that when it reaches the data scientists, it is ready for use. They have to gather data from different sources and store them up in one warehouse. That warehouse represents the collected data uniformly. 

It does sound easy, but such things require a various set of skills. That’s why this role is highly in-demand among tech industry jobs. Data engineering jobs rank up high right next to data science and machine learning.

Managing and Optimizing Core Data Infrastructure

There is a lot of real engineering to do in this area. One of the key things is ensuring that the data technology is working at its top results, especially when it comes to improvement to cost or performance.

Usually, that consists of the following:

  • the making of monitoring infrastructure so that the pipeline’s status can be visible
  • keeping an eye on all jobs to monitor the performance
  • running maintenance routines
  •  trying to lower the cost and improve the performance.

Sometimes these assets don’t focus on enough in the earlier stages of a data team, but they become more crucial as the project grows.

For example, data engineers working at Uber have built something that’s called Queryparser. Query parser monitors all queries and automatically gathers statistics and patterns. They can use this data to tune infrastructure accordingly.

Data engineers are also responsible for building tools that are not available for them to buy. That’s why, for example, Airbnb had to make Airflow since there wasn’t a way to schedule DAGs.

Building and maintaining ingestion pipelines isn’t an easy thing to do. It’s always safe to assume that it will take more time and resources to do so than you initially intended. Making a pipeline that is regularly delivering data to your warehouse is the most challenging thing.

Does Your Startup Data Team Need a Data Engineer?

The short answer is yes. The importance of having a data engineer on a data team keeps growing and growing. To that end, companies and startups need groups of people that will entirely focus on refining data so that data scientists can get value from it. Data engineers are an essential part of any functioning data team.

Over the past ten years, most companies have gone through a digital transformation. As a result, new types of data emerged. Someone needed to keep track of data’s quality and security for data scientists to do their job. So, as a result, it is critical to have a data engineer so that other data-related positions can work properly.

Even today, with the digital transformation going on and the rise of AI, it has become clear that there is a need for data engineers. Usually, this is why we see the escalation of data engineering. Companies and startups need a team that will focus on data – and that will also make sure that there is a way to extract value from it.

What Kind of Skills Does Your Data Engineer Need? 

When we think about this question, the first answer that pops to our head is probably: A data engineer needs to have a software solution for data. It is too much to expect a data engineer to know all the technologies a software developer uses. Even more, these technologies do change frequently.

Some of these technologies have been around longer than others. For instance, SQL has been around for some time now. However, some technologies are no longer in use at all.

It is necessary to take into account that data science and data engineering are two different things. To make things more understandable, we can think of data engineers as people with data engineering skills.

According to Jeff Hale, an instructor when it comes to data science and data engineering, some of the most critical skills would be:

  • Programming – a language that has become popular amongst data engineers is Python.
  • Foundation software engineering – architecture design, server-oriented architecture
  • SQL – it will always be of importance for databases
  • Distributed systems – various software engineering skills
  • Analytics – Various mathematical principles might be crucial to shaping the data to be of use to the people who are analyzing it.
  • Data modeling – a data engineer needs to know this since they’ll need to know how to structure tables, partitions.

How to Hire Your First Data Engineer?

As with almost every other job, you would want your data engineer to have previous experience. Of course, this is not something that will dictate whether you will hire a data engineer or not.

It would be ideal if your potential data engineer:

  • has worked on Big Data projects in the past;
  • has worked as a software engineer at a certain point in their career;
  • has recommendations from other companies;
  • has worked in the same or similar fields.

It is not easy to set apart good candidates from the bad ones just by doing an interview. The focus should be on the skills that the candidate can present during their technical interview.

Another important thing about hiring your first data engineer is – to be very clear about what you are looking for. A common misjudgment is that people mistake data science with data engineering (and vice versa). There are cases where startups will look for both in one person. They do exist but are somewhat harder to find.

If you don’t have a data scientist nor a data engineer, it would be wiser to hire a data engineer first. By doing so, you can ensure that once you hire a data scientist – they have something to work with.

The Effects of a Bad Hire

Lousy hiring can affect the whole team. Even just one bad hire can bring unwanted problems to the team. Let’s say, for example, that your team has four people. If you have one person that is not quite skilled enough – your team will only have 75% productivity.

It is of great significance to hire people who know what they’re doing and are willing to learn more. In most cases, it is not enough to browse the internet and rely on online answers. Some things that are a bit more complex require knowledge and experience.

Therefore, it’s necessary to be careful while hiring, especially in the early phases of having a startup. Having the right (or wrong) employees can dictate the future of your company.

The cost of ineffective hiring can be very high, especially if you have a startup. Not only will you lose the money that you invested, but also your time – since you’ll have to supervise present and future employees.

However, time and money aren’t the only things that are on the line. The cost of a bad hire can also be:

  • Productivity – A bad hire can lead to lower productivity amongst the whole team.
  • Worse relationships with your clients – Your clients might see the lack of quality in your production, leading to them not trusting your company (startup) anymore. It could also damage your relationship with future clients.
  • A different view on your company – By having unsatisfied clients, your whole company can get a bad reputation.
  • Decreased teamwork – With having just one employee, that’s a bad hire. The team can not work in full potential. This may result in reduced collaboration. 


Finding a decent data engineer can be a difficult job. For every data scientist you have, you would need multiple data engineers. The previously mentioned can make things even harder. But it’s better to have the right people for the job. It can ensure the success of the company’s project.

We have mentioned how it is possible to have both a data engineer and a data scientist in one person. But the truth is, it is better to separate those two roles – especially in companies that work with large amounts of data.

In the end, for the whole data team to work decently (or even the entire company), it is of great importance to have a data engineer. For instance, pipeline and infrastructure have to be in order before a data scientist can use any data and do their job. Not having the right data can cause other problems, such as algorithms not working correctly or even cause biases.

About Author:
Michael Yurushkin
Ph.D. in Physics and Mathematics
CTO & Founder