The Data Engineer’s role is to prepare the data for the Data Scientist to analyze. Big Data and Data Science are growing, and more and more jobs are emerging in this field. Today, we’re going to take a closer look at one of the three main data science jobs, alongside the roles of Data Scientist and Data Analyst : the Data Engineer.
What are the roles and responsibilities of the Data Engineer ?
The Data Engineer is an engineer. His role is therefore to design and manufacture. However, rather than aircraft or buildings, they specialize in data. More precisely, in data pipelines.
His responsibility is to collect raw data from multiple sources into a centralized data warehouse. He is responsible for designing and managing the organization’s databases and data lakes.
He must set up a pipeline to automate the various stages of data acquisition, from extraction to storage. In a second step, the Data Engineer “cleans” the data and transforms it. The objective is to make it ready to be analyzed by the Data Scientists.
Thus, the Data Engineer does not work alone. He is part of a team, and his role is to support the Data Scientists by providing them with ready-to-use data. The latter can then run queries or launch their Machine Learning algorithms to analyze the data.
The Data Engineer must also create tools and algorithms that allow the Data Scientists, and eventually other employees or managers in the organization, to easily access the data they need.
What are the missions of the Data Engineer ?
The tasks of the data engineer vary from company to company. However, as a general rule, he or she is entrusted with four main missions.
The first is to develop and implement the processes for collecting, organizing, storing and modeling data. He is therefore the main person in charge of the company’s data infrastructure.
The Data Engineer must also ensure access to the various sources and the quality of the data. In addition, he has to ensure that the company’s data analysts and data scientists can easily access the data and exploit it under optimal conditions.
Data Engineers are often found in a DevOps role : they are in charge of putting into production the predictive models created by the Data Scientists.
Finally, under the leadership of the Chief Data Officer and the Data Management Officer, they are responsible for implementing a data policy that respects current regulations.
What are the skills of the Data Engineer ?
The Data Engineer has a wide variety of skills. First of all, he masters data languages such as SQL, and database management tools. These tools allow him to manage databases and to perform queries.
Depending on the technologies used by the company, other query technologies such as Cassandra and BigTable can be of great help. Indeed, many organizations are not satisfied with just one query technology.
Recently, a new method called “ELT” (Extract, Transform, Load) has emerged. It reverses two steps in the ETL process : “Transform” and “Load”. By loading the data before transforming it, it is accessible at any time. This new method is adapted to the increasing volume of data pools and the emergence of cloud storage.
The data engineer must also handle data storage and ETL tools. These tools are at the heart of the function, as they allow to aggregate data from various sources and to transform them.
The mastery of Hadoop-based analysis solutions, such as Hbase and Hive, is more and more expected from a Data Engineer. Even if his role is not that of a Data Scientist, companies expect him to be able to analyze data with a view to monitoring its quality. In some smaller organizations, the roles are less distinct and the functions of Data Scientist and Data Engineer sometimes merge.
Knowledge of mathematical and probabilistic principles of analysis is necessary to manipulate data and transform it correctly. Similarly, notions of data modeling are required to know how to structure tables and partitions or restore certain attributes.
A data engineer must master a general-purpose programming language such as Python, Java or Go and possibly have knowledge of more specialized languages such as Scala, Julia or Perl. These languages allow him to develop data pipelines, implement statistical models, perform analyses or produce dashboards and data visualizations.
Today, Data Engineers must also have a vision of what Machine Learning, Deep Learning and Artificial Intelligence are. These technologies remain the field of expertise of Data Scientists, but here again, the engineer must understand them to be able to assist them.
As companies are massively turning to Cloud Computing, a Data Engineer must master Cloud platforms such as AWS, Google Cloud, Microsoft Azure and their various Big Data services.
Finally, with a view to putting Data-driven projects into production, the job must be familiar with certain DevOps tools: versioning tools, virtualization tools, APIs, monitoring and automation tools…
Beyond these concrete skills, one of the main qualities of the Data Engineer is to know how to quickly master an unknown technology. This is what will allow him to face the incessant emergence of new technologies in the fast growing field of Data Science.
About soft skills, the Data Engineer must have a sense of communication in order to collaborate with other departments and understand the objectives and needs of management.
What are the salaries and job opportunities ?
According to Glassdoor, the average data engineer in the U.S. makes $137,776 per year. Salary ranges from $110,000 to $155,000 per year depending on skills, experiences and location.
Senior Data Engineers earn an average of $172,603 per year. Their annual salaries range from $152,000 to $194,000.
In France, the average annual salary is significantly lower. Again according to Glassdoor, it is around 43,850 euros.
In Deutschland, the average annual salary is a bit better than in France with an annual revenue of 62k euros.
With the explosion of Big Data, Data Engineers are increasingly sought after by companies in all sectors. Since 2012, the number of jobs has increased by more than 400% and almost doubled in 2016.
This is due to the explosion of data volume, its increasing exploitation by companies, and the increasing complexity of data processing technologies. In the future, we can expect the role of Data Engineer to become more and more required in companies.
