Data engineering is an important component of the ecosystem of data, and it consists of diversified professionals who play basic roles in data management and processing. Although the job title may be the same, as I have seen over the years, data engineers are often located in distinguished models: “Businessy” data engineer and “technical” data engineer as I like to call them. In this blog post, we will explore these two models, their characteristics, and their contributions to the world of data engineering.
Business data engineer
These people are everything about solving work problems. They are excited to track standards, major performance indicators (KPIS), and build interactive information panels. Often, they have a wide SQL experience and they have coding skills in multi -user languages such as Python, ideal for data processing and analyzing it.
ResponsibilitiesTheir basic focus on translating work needs into data solutions. They build data pipelines to collect, transfer and download data, and enable meaningful visions for decision makers. These professionals are often referred to as business intelligence engineers (BI).
Daily tasks: It may include a typical day to collect the requirements of stakeholders, design information panels, and text programming in Python or SQL to extract and transform data, and cooperate with work teams to ensure data -based decisions.
Technical data engineer
On the other hand, technical data engineers are withdrawn to solve domain problems. They flourish in exploring and implementing new technologies, and they often prefer coding in languages like Scala or Java. They are responsible for building developmentable data pipelines that can handle large quantities of data.
ResponsibilitiesTechnical data engineers focus on building and maintaining strong infrastructure for data. It guarantees that the data pipelines are developed, reliable and capable of dealing with large data groups. It is adept at tools like Apache Spark, Apache Flink and Apache Airflow, which is vital to processing huge amounts of data and defines the complexities of cloud tools.
Daily tasks: A typical day for the technical data engineer may include improving data pipelines, exploring and repairing errors in performance, experimenting with new data storage and processing techniques, and cooperation with data scientists to publish automated learning models.
Bridge
While these two models of data engineers have distinct roles and responsibilities, there are enormous potentials when they work together. The Businessy Data Engineer’s ability to understand and translate work requirements complete the experience of the technical data engineer in building developed solutions. People who want people want to want, and technical people build data strength to support these needs. Cooperation between these two types of data engineers can create strong data -based solutions. Collective work makes the dream work, right?
Now, this is the thing: most of the data engineering training focuses on the technical side of things, leaving a Businessy data engineer. We need content that displays their role, helps individuals in determining the appropriate job leaflets, and directs them on their learning journey.
What kind of identification is?
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