Data Scientist vs Data Engineer vs Data Analyst vs Data Architect

Each stakeholder has a clear role to play for a business problem right from understanding the essentials of the problem, proper planning, implementation of the project, analyzing the various outcomes of the project, solving the bottlenecks visible in the outcomes, and generating reports by drawing inferences about the success of the project. The various key stakeholders in any project include the Data analyst, the Data scientist, the Data engineer, the Database administrator, and the Analytics manager.

A lot of us get a bit confused with the roles and responsibilities of a Data Architect, Data Analyst, Data Engineer, and Data Scientist. So let’s try to understand how each role is different from one another.

Data Engineer :

The role of a Data Engineer is not to analyze data but rather to prepare, manage and convert data into a form that can be readily used by a Data analyst or Data scientist.

Data Architect :

A Data Architect provides the support of various tools and platforms that are required by Data engineers to carry out various tests with precision. A Data architect should be well equipped with knowledge of data modeling and data warehousing. Other additional skills required by a Data architect are Extraction, Transformation, and Load (ETL), and knowledge of Hive, Pig, and Spark.

Data Analyst :

A Data analyst is to extract data and interpret the information obtained from the data for analyzing the outcome of a given problem in business.

Data Scientist :

A Data scientist incurs all the skills of a Data analyst with the additional skills of data wrangling, complex machine learning, Big Data tools, and software engineering. It is observed that both Data analysts and Data scientists use the same tools and practices.

Hope this was helpful.