The growing number of startups all around the globe has surely caused an exponential increase in the job opportunities. However, professionals with a certain skillset are more in demand as compared to the others. The role of data engineers serves as a classic example in this regard. With more and more organizations making a shift from hierarchical to a data-driven culture, data has become an integral part at each stage of an organization’s formation. In view of this fact, we will try to answer the following questions in this write-up:

·       What Exactly Does a Data Engineer Do?

·       Why is Hiring Data Engineers better-suited for Startups?

What falls under a Data Engineer’s Job Description?

To put it simply, a data engineer is someone who is responsible for the movement and storage of data. In view of this goal, they develop, test, and maintain a database in a scalable manner. Another responsibility that comes as an overlapping one in the data engineer vs. data scientist debate is data cleansing. Although data engineers are responsible for enhancing data quality, a look at the stats reveals that data scientists spend 80% of their time in this practice. Such is the importance of this step and it leads to data engineers being catchier for the startups as they cover overlapping responsibilities of a data scientist’s JD. To keep a clear differentiation, refer to the image below:

Figure 1 Image Source: https://www.google.com/url?sa=i&url=https%3A%2F%2Ftwitter.com%2Fmytechbf%2Fstatus%2F1448308288639152132&psig=AOvVaw20GYuUayrypxw1Uq1iMwLW&ust=1652907437030000&source=images&cd=vfe&ved=0CA4Q3YkBahcKEwjQ2_-Ntuf3AhUAAAAAHQAAAAAQEg

Apart from that, there are other overlapping tasks when we talk about data engineers and data scientists. For instance, a data engineer uses the developed data sets in order to address a wide range of business requirements. The deployment of various analytics programs along with machine learning also refers to an overlapping task. This is why veterans recommend going for a data engineer when you are on a budget.

Taking this discussion a step further, let us ponder over the factors that make data engineers a necessity for startups.

What Makes Data Engineers A Viable Option for Startups?

A number of reasons compel startups to opt for a data engineer rather than a data scientist. Elucidating it further, let us take a deeper look at the reasons leading to startups taking a greater interest in data engineers and how it has transformed their role.

Startups not having Large Amounts of Data

When you have to play with data while you just setup your organization, it is obvious that the amount of data is small. If you look for a data scientist at this stage, it might lead to you rethinking your strategy after some time as a data scientist works on the expansion of the data organized by a data engineer. Therefore, if you do not have large data that needs maneuvering, hiring a data scientist might not be a viable option as there would not be enough data to work on, raising the importance of data engineers for startups.

Amalgamation of Responsibilities in a Single Role

Another attractive opportunity a data engineer brings for a startup is that at an initial stage, they can act both as a data engineer as well as a data scientist. This factor resolves the scalability issues for an organization while moving forward. As the tasks overlap, a data engineer covers significant amount of a data scientist’s JD. Going this way, you can take two problems down with a single blow.

Data Engineering Better Aligned with Other Roles

Hiring a data engineer also aligns quite well with the other members of the team at the start. This is because when you hire a data scientist in the initial phase of your organization, tasks assigned to other team members start overlapping with their role. This leads to a ruckus among the organization. On the contrary, role of a data engineer is pretty much defined. A professional who will develop, test, and maintain your database would suffice. Add the margin to tweak the job description as per the requirement and you are good to go. This does not meddle with the tasks assigned to other team members.

Above All, a Cost Effective Option

Being affordable as compared to the data scientists has transformed the role of data engineers in startups by making them the preferred option. Whenever we talk about startups, financial constraints always hamper many activities during the initial phase. In contrast, a super qualified data scientist on average draws an annual salary of around USD 200,000 as per the stats. Meeting operational expenses is an uphill task at the start. Therefore, you would want to opt a more affordable option to address your data needs rather than going all out for a data scientist.

Complex Activities are not Usually Required Initially

It is a rare chance that your startup will be going to require the creation of AI pipelines, machine learning, and data optimization for forecasting right from the start. These are the core functional areas for a data scientist on which they can work only when a data engineer has gathered and performed ETL on the acquired data. This alone acts as a major reason in data engineers gaining an upper hand in the data engineers vs. data scientists selection for a startup.

The Takeaway

With every implementable idea transforming into a startup, the number of startups brewing from incubation centers is seeing an unprecedented rise. As this happens, the role of data engineers is also seeing a transformation in the form of an expansion of their job description. Importance of hiring data engineers for startups can be determined by taking into account them being affordable as well as their ability to deploy statistical methods and machine learning. Going forward, the opportunities in startups can act as a great learning curve for data engineers as this role is what leads to data scientist becoming a requirement for an organization. In the times to come, we may see data engineers evolving into data scientists as working with the same organization will give them a firm grip on the domain they are working on. Furthermore, the data to be worked upon will be developed and tested by their own hands. This would make expansion and retrieval of insights for forecasting a lot more convenient. When you combine an architecture aligned with business requirements, data developed, tested, and cleaned by the same professional, they can work on its expansion in a far better way as compared to a newly hired data scientist.