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Data engineers are critical contributors to any enterprise data analytics team. They primarily are responsible for bringing information in various forms and locations together by creating data pipelines. They work primarily with “big data,” which is defined as larger and more complex datasets that cannot be handled in traditional data processing software. Source.
Data engineers design and construct systems for collecting, storing, and analyzing big data. The quantity varies based on the company and industry, but data engineering has applications in virtually every field.
What is a data engineer?
A data engineer is a specific type of software engineer. Their main function is to manage and convert an organization’s data so that it can be used and understood by data scientists. The ultimate goal of a data engineer is to improve accessibility of data to the company so that critical business decisions can be made based on facts and numbers.
Data engineering is closely linked to, but not the same as, data science or data analytics. Data engineering is the practice of designing, building, and maintaining data infrastructures and platforms. These include databases, big data repositories, and data pipelines, which are used to transfer data between various systems.
The engineer makes the data available to be analyzed by optimizing the systems. The analyst then uses the results of the engineer to derive insights and meaning. The scientist then takes the analysis even further by developing complex predictive models to solve further data questions. Source. The graphic below explains some of the various data management roles a company may have.
Data Engineer Role & Responsibilities
As a data engineer there are certain tasks you can expect to be part of your typical role no matter your organization. These include the following:
- Obtain the necessary datasets for the business
- Create algorithms that convert data into valuable, actionable information
- Develop, test, and manage database pipeline architectures
- Gain thorough understanding of company objectives by working with management
- Develop new data validation and analysis systems
- Ensure data governance complies with security policies
Data engineers are responsible for taking raw data and formatting it so that it meets the requirements of the data scientists and stakeholders. The initial data they begin with may contain human or machine errors and will be unformatted and full of system specific codes. It is the engineers job to sift through this information and “crack the code” on the disordered data.
The overall purpose of this is to get big data into a user-friendly format so that it can be applied to everything from sentiment analysis to machine learning, and a variety of other data actions.
These roles may also be dependent on the size of the company. Larger organizations often have data engineers focused on one or two specific tasks, such as building or testing data pipelines. On the other hand, smaller companies will typically have their data engineer(s) take on a wider range of roles.
Data Engineer Demand & Salary
More data than ever is being produced, stored, and utilized in our modern world, especially big data. Therefore, the demand for a data management team at virtually every organization has grown drastically. This means that the need for data engineers is at an all time high. They are truly the core of all data analysis so it is no surprise data engineers can make a pretty comfortable living, providing such a crucial service.
The demand for data engineers has soared since just 2018. In 2018, data engineering was ranked 33 out of 50 on Glassdoor’s most sought after roles in the United States. By 2020, it had climbed to number 6 on this list.
This rise was reflected in the mean salary as well, increasing from $100,000 in 2018 to $112,493 in 2021 according to Glassdoor. A few other job listing sites have slightly different estimates, such as Indeed suggesting $117,135, Salary.com providing $109,194, and PayScale with the most conservative estimate of $92,934. Regardless, all of these estimates are very respectable salaries and suggest one can expect to make at least $100,000 as a data engineer.
There are some other obvious factors that contribute to an individual data engineer’s salary. These include, amount of prior experience, geographic location, job title, and company type and size. Entry level data engineers, as expected, are likely going to earn considerably less than a senior data engineer. However, most estimates for entry level positions are still not much under $100,000. Additionally, location can be a large factor. It is estimated that the US, Germany, and Australia provide the average highest wages for data engineers. With the Netherlands, France, and Canada coming in close behind. Source.
At the current growth rate it is reasonable to expect the demand and salary of data engineers to continue increasing steadily. The data realm is a very promising industry to get into with the expansion of big data in recent years.
Conclusion
In our modern world, data engineers play a critical role in harnessing and unleashing the power of a business’s data. They manage a company’s big data, which cannot be done in simple software systems. Data engineers develop, test, and maintain repositories, pipelines, and other platforms to bring together an organization’s most complicated data and convert it into an easily analyzable format for data scientist and analysts. They are the backbone of data science and support the optimization of any company through educating business decisions with real numbers. Data engineers are in high demand, as more and more companies are seeing the value in investing in their data management.
Reach out to QBIX Analytics to find out more about data engineering and how they can help you discover and execute your company needs.