The demand for skilled data engineers is projected to rapidly grow. No wonder that’s the case; no matter what your company does, to succeed in today’s competitive environment, you need a robust infrastructure to both store and access your company’s data, and you need it from the very beginning.
What exactly does a data engineer do, though? And how does one become a data engineer? In this article, we’re going to talk about this interesting field and how you can become a data engineer.
Data engineers are responsible for the creation and maintenance of analytics infrastructure that enables almost every other function in the data world. They are responsible for the development, construction, maintenance, and testing of architectures, such as databases and large-scale processing systems. As part of this, Data Engineers are also responsible for the creation of data set processes used in modeling, mining, acquisition, and verification.
Engineers are expected to have a solid command of common scripting languages and tools for this purpose and are expected to use this skill set to constantly improve data quality and quantity by leveraging and improving data analytics systems.
While there is a certain amount overlap when it comes to skills and responsibilities, these two positions are being increasingly separated into distinct roles.
Data scientists are much more focused on the interaction with the data infrastructure rather than the building and maintenance thereof. They are often tasked with conducting high-level market and business operation research to identify trends and relations, and as part of this, they use a variety of sophisticated machines and methods to interact with and act upon data.
Data scientists are often well-versed in Machine Learning and advanced statistical modeling, as they are expected to take the raw data and turn it into actionable, understandable content with the help of advanced mathematical models and algorithms. This information is often used as an analysis source to tell the “bigger picture” to the decision makers.
So what makes a data scientist different from a data engineer? Generally speaking, the main difference is one of focus. Data engineers are much more focused on building infrastructure and architecture for data generation; data scientists are focused rather on advanced mathematics and statistical analysis on that generated data.
Here's a couple of the key skills needed from data engineers.
Since data engineers are much more concerned with analytics infrastructure, most of their required skills are, predictably, architecture-centric.
Data Engineers need to understand database management, and as such, in-depth knowledge of SQL is hugely valuable. Likewise, other database solutions, such as Cassandra or Bigtable, are great to know if you plan on doing freelance or for hire engineering, as not every database is going to be built in the recognizable standard.
Data warehousing and ETL experience is essential to this position.
