DATA ENGINEER

They use a systematic approach to plan, create, and maintain data architectures while also keeping it aligned with business requirements. 

Before initiating any work on the database, they have to obtain data from the right sources. After formulating a set of dataset processes, data engineers store optimized data. 

Data engineers conduct research in the industry to address any issues that can arise while tackling a business problem. 

Data engineers don’t rely on theoretical database concepts alone. They must have the knowledge and prowess to work in any development environment regardless of their programming language. Similarly, they must keep themselves up-to-date with machine learning and its algorithms like the random forest, decision tree, k-means, and others. 

They are proficient in analytics tools like Tableau, Knime, and Apache Spark. They use these tools to generate valuable business insights for all types of industries. For instance, data engineers can make a difference in the health industry and identify patterns in patient behavior to improve diagnosis and treatment. Similarly, law enforcement engineers can observe changes in crime rates. 

Data engineers use a descriptive data model for data aggregation to extract historical insights. They also make predictive models where they apply forecasting techniques to learn about the future with actionable insights. Likewise, they utilize a prescriptive model, allowing users to take advantage of recommendations for different outcomes. A considerable chunk of a data engineer’s time is spent on identifying hidden patterns from stored data. 

Data engineers dive into data and pinpoint tasks where manual participation can be eliminated with automation.