What is a Data Engineer?
A data engineer is an individual responsible for managing, optimizing, overseeing, and monitoring data retrieval, storage, and distribution.
- A data engineer is tasked with managing, optimizing, overseeing, and monitoring data retrieval, storage, and distribution.
- Their role typically falls into one of three categories: generalist, pipeline-centric, and database-centric.
- Data engineers prepare the big data infrastructure that is then analyzed by data scientists; they are not the same.
Roles of Data Engineers
The roles of data engineers will usually vary depending on the type of company that they work for and the specific industry. However, they can broadly be categorized into three main categories: generalist, pipeline-centric, and database-centric.
Generalist data engineers usually work on a small team with other individuals with data science expertise, such as scientists and data analysts. When data engineers are one of the few or the only data-focused employees at their workplace, they will likely need to do more end-to-end work, such as following through with the entire process of ingesting the data, processing it, and getting involved in data analysis.
Pipeline-centric data engineers are often found in larger, midsize companies. They are responsible for working with other data scientists to interpret and use the data collected. The larger companies usually need to deal with more complex needs compared to the generalist data engineers mentioned previously. As such, they usually work in teams as the work entails an in-depth knowledge of computer science and data systems.
Database-centric data engineers are found in some of the largest companies and conglomerates, and their job is to focus on setting up and populating analytics. There are usually large databases involved, and the data engineers work with data warehouses across multiple databases.
Skills and Responsibilities of Data Engineers
The position of a data engineer is a technical one and requires considerable experience and skills in areas such as programming, mathematics, and computer science. However, they also need to be good communicators in order to communicate trends to others within the organization and explain any issues or inconsistencies that they notice to those with less expertise in the area.
The most common responsibilities of data engineers include:
- Data acquisition
- Developing, constructing, testing, and maintaining architectures
- Aligning architecture with business needs
- Using programming language and tools
- Identifying ways to improve data quality, efficiency, and reliability
- Using large data sets to address business issues
- Preparing data for predictive and prescriptive modeling
- Find hidden patterns using data
- Identifying inconsistencies within the data
Data Engineers vs. Data Scientists
Data engineers and data scientists are sometimes confused by members of the public and those without too much knowledge of the field. In order to avoid confusion, it is important to note that data engineers are the data professionals who prepare the big data infrastructure, which is then analyzed by data scientists.
Data engineers help design, build, and integrate data from various sources and then write complex queries. The overall goal is to ensure the best performance of the organization’s big data ecosystem. They also create big data warehouses that can be used for reporting or analysis by data scientists. In contrast to data scientists, data engineers focus more on the design and architecture and are typically not expected to know any machine learning or big data analytics.
On the other hand, data scientists are individuals who apply statistics, machine learning, and analytic approaches to solve important business problems. Their main objective is usually to help organizations turn their volumes of big data into valuable and actionable information that can be used. Data scientists tend to be more well-known to the public due to their customer-facing roles, while data engineers deal with more of the work from behind the scenes.
It must be emphasized that data scientists are only as good as the data they are able to access, which highlights the importance of data engineers and their roles. A simple analogy of the roles of data engineers versus data scientists would be that of a race car builder compared to a race car driver.
The driver gets the excitement of racing their car along a track and gains fame and popularity in front of the crowd watching them. The race car builder’s role is no less important, although it is performed behind the scenes with turning engines and experimenting with different setups to create a powerful machine that the driver can race.
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