What is Data Engineering?
Data Engineers have multifaceted responsibilities. These responsibilities include, but are not limited to,
working on reports, dashboards, data pipelines, and optimizing Python code to run it in C or Java. Even though data science as a career picked up steam of late, the demand for data engineers has also increased with time. Most companies keep their data stored in multiple formats across databases and text files. Data engineers develop a process to transform that data into formats that can be analyzed and used by data scientists.
They design, build, combine data from different resources, and maintain big data. Then, they do complex queries on that, ensuring the accessibility and smooth running of the system. They optimize the performance of their organization’s big data ecosystem. Additionally, they run big data ETL (Extract, Transform, and Load) and build big data warehouses that data scientists can use for reporting or analysis.
There’s a high demand for data engineers, but that also emphasizes the need for finding the right talent. A simple solution to address this need would be to assess candidates using a data engineer test.
What differentiates a data engineer from a data scientist ?
Data Engineers work towards building architecture and infrastructure for data generation, whereas data scientists can perform mathematical and statistical analysis on that generated data. Data Engineers build robust, scalable infrastructure for providing intriguing business insights from primary data. They handle complex analytical projects and devise real-time analytical solutions. They also support data scientists by providing tools and infrastructure that are vital to solving business problems.
Simply put, data scientists rely on the services of data engineers to perform essential business operations. They extensively use advanced analysis tools such as R, Hadoop, SPSS, and advanced statistical modeling. Data Engineers, in turn, make use of these services: SQL, Cassandra, MySQL, and NoSQL. The lines are getting blurred between data scientists and data engineers, as both of them are programmers at heart. Having prowess in both fields is becoming mandatory for professionals. That’s why electronic assessment (e-assessment) like data science assessment and big data assessment provides a secure medium to assess the professional skills, knowledge, and abilities beforehand.
Why should you use Mercer | Mettl’s Data Engineer test?
Online data engineer assessment helps recruiters and hiring managers to scientifically evaluate data science and data engineering concepts of the candidates. This technical aptitude test offers candidates to take the test from anywhere with the utmost convenience. The test includes programming simulations for big data engineers, data scientists, and similar roles and helps recruiters zero in on the best-fit talent both culturally and skillswise. Such tests include important questions in associated skills such as Hadoop - Hive and Spark, R Programming, MongoDB, SQL, Data Modeling, and Data Warehousing.
Data engineering assessment contains 25 questions to be solved in the time duration of 35 minutes. Primarily a set of standardized questions, the data engineer test is the result of the cumulative efforts of many contributors, including subject matter experts and well-versed professionals. You can also create customized assessments according to the job role.