Career as a Data Engineer: Scope, skills needed, job profile and other details
Who is data engineer?
Data Engineer is a person who is responsible for managing data workflows, pipelines, and ETL processes. As the denomination suggests, “Data Engineering”, denotes it is associated with data, namely, their distribution, storage, and processing. In short Data Engineer is a person who collects, move, store, and pre-process the data for Data Scientist and Data Analyst.
What does data engineer do?
Data engineers involve in preparing data for analytics or operational users. They withal build data pipelines to pull all the information together from different sources.
The aim of a Data Engineer is to make data secure and accessible for data scientists and analysts so that they can analyze it felicitously. Data engineers deal with raw data that often contains an abundance of errors.
Data engineers use sundry implements and ways to ameliorate the quality, reliability, and efficiency of data. You will understand more about Data Engineering in the next section- Roles and Responsibilities.
Qualification required for a data engineer
As a Data Engineer, you just need an Undergraduate degree in Computer Science, IT, Software Engineering, Math, or a business-cognate field. So, this is the required qualification for Data Engineers, but only having a degree is not enough. You should have some required skills in order to become a Data Engineer.
Skills required to become data engineer
Data engineers need to be comfortable with a wide array of technologies and programming languages. These are perpetually subject to transmute, so one of the most consequential skills that a data engineer possesses is the underlying cognizance for when to employ which language and for what purport. Data engineers must be fascinated with perpetually updating their technical adeptness-sets. A good data engineer will possess erudition of and skills in all the following:
- Building and designing astronomically immense-scale applications
- Database architecture and data warehousing
- Data modeling and mining
- Statistical modeling and regression analysis
- Distributed computing and splitting algorithms to yield predictive precision
- Proficiency in languages, especially R, SAS, Python, C/C++, Ruby Perl, Java, and MatLab
- Database solution languages, especially SQL, as well as Cassandra, and Bigtable
- Hadoop-predicated analytics, such as HBase, Hive, Pig, and MapReduce
- Operating systems, especially UNIX, Linux, and Solaris
- Machine learning, including AForge.NET and Scikit-learn
Conclusion:
Skills for any expert relate to the obligations they’re responsible for. The range of skills would vary, as there is a wide range of data engineer key skills. However, for the most part, their tasks can be arranged into three primary territories: engineering, data science, and databases/warehouses.
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