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The data engineer prepares the infrastructure for the “big data” that the data analyst will then be able to work with. They develop algorithms in order to collect raw data that corresponds to the business goals of their company.
In more detail
Overall, a data engineer is in charge of managing data workflows, pipelines, and ETL (extract, transform, load) processes. Generally, they work with very large streams of data.
Some of their tasks may include preparing data pipelines for data scientists, and working with batch/streaming pipelines for event-related data. In order to do this, data engineers are tasked with designing a reliable infrastructure for data collection, storage, and processing. As such, this position is heavily involved in backend engineering. Subsequently, some consider it to be a subset of backend engineering, and similar positions are often listed as backend engineers that specialise in data.
In order to work in this field, it is necessary to have in-depth knowledge of algorithms, data structures, and information systems. As a result, successful candidates applying for data engineer positions typically have at least a bachelor’s degree in Computer Science, Statistics, Math, or related fields. However, a master’s degree is preferred.
Further knowledge of Python and Java is also a great asset; tools for storing and processing large datasets are usually written in these languages (such as Apache and Hadoop). It is also important to have some knowledge of big data tools, with the most popular being Spark and Kafka.
Finally, it is also good to be familiar with A/B testing in order to verify systems’ performance and reliability.