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Big Data Testing: Data Quality Engineer

Companies use accumulated data to boost sales, profits, and advertising is an aspect of the technology revolution. High-quality data and skillful brains that can make money from them (correctly process, visualize, build machine learning models, etc.) have become the key to success. Today, the business has received an enormous need for data quality engineers who would monitor the data pipelines in the system, the quality of data at the input and output would conclude their sufficiency, integrity, and other characteristics.

Who is a Data Quality Engineer

Checking the quality of information extracted using big data is the primary goal of experts in data quality. Data Quality Engineer verifies the information that is easy to use, meets business requirements, and meets established quality metrics. He also builds processes for automatic data checks at different levels of the system and stages of its development. This work is essential for companies because if the data is not qualitative, the analyses will not have the necessary validity, and the strategic decisions based on this information will not be reliable.

Data Quality Engineer Key Features

• Identify and participate in data quality checks

• Obtain quality indicators

• Check for information inconsistencies during analysis

• Report incident

• Ensure data consistency between applications

• Pre-verify massive arrays of information and eliminate doubts about the origin of the data

• Monitoring quality measurement tasks and support

• Preparation of monitoring reports

Data Quality engineers manage data during their daily work, check its behavior in new conditions and systems, and control the relevance of data and its sufficiency. At the same time, Data Quality engineers devote a little time to classic functional testing.

The duties of the Data Quality Engineer are not limited to routine manual/automatic checks on "nulls, count, and sums" in the database tables. Still, they require a deep understanding of the customer's business needs and, accordingly, the ability to transform the available data into relevant business information.

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