Modern-day data requires enterprise organizations to employ an entire team of data scientists, engineers, and analysts to process and analyze it. Why?
Because data is growing at an unprecedented rate, and it comes in all shapes and sizes–structured, unstructured, and semi-structured.
Today, enterprise companies are using this data in innovative ways, including:
However, data requires a lot of processing before it can be used. In its raw form, data makes little sense. It needs to be structured and shaped, and for this, companies need data experts.
Data engineers are experts that specialize in data handling and processing; they are tasked with creating data migration pipelines and constructing data warehouses from the ground up–but more on that later.
Building a data team can be difficult, and several factors must be considered. In this article, we will discuss the different experts required for a data team and how each constitutes data management and mobility within the company. We’ll also discuss the different structures a data team can follow. Finally, we will see how the data team can be expanded to meet company requirements.
Data used to be a straightforward entity; all you needed was someone with basic database knowledge to carry out your operations. However, things are quite different today. For specialized tasks, you need a team of technical people. A proper hierarchy is required to create an effective data engineering team.
Let's discuss some of these data magicians below.
With years of experience working with data, they will provide you with insights, define policies, procedures, and strategies, offer consultation on what can be achieved from the available data and create a road map for organizing and structuring your data.
In enterprise organizations, data engineers are tasked with creating data warehouses and lakes. These infrastructures are required when the velocity and volume of data grow to a point where conventional databases are no longer feasible.
Data engineers are usually the first hires when creating a champion data team. These engineers are pivotal in building an organization's data infrastructure, and all other teams depend on their work. Overall, the data engineering team can have multiple responsibilities depending on the requirements and business needs of the organization.
Let's discuss these responsibilities in detail.
The data engineering team members utilize their ETL skills to load data into this centralized repository periodically. Various data engineering tools are also available to carry out this process.
A data warehouse only stores structured data, while a data lake can store both structured and unstructured data. Additionally, decentralized data processing technologies such as Apache Hadoop or AWS data lake are required for building massive data lakes.
The kind of structure your data engineering team needs depends entirely on your organization's requirements. Let's discuss these individually to see how each of them operates.
This mode of operation has certain benefits, such as more accessible communication between the different data teams and a more structured chain of command.
There are also certain downsides, such as a communication gap between the data and non-data teams within the organization, leading to delays in critical reports and projects.
This mode of operation is more beneficial for individual teams as there are minimal delays in work, and requirement gathering is more accessible due to the elimination of the communication gap across teams.
The downside to such a data engineering team structure is the lack of mentorship. Serving as a sole data professional means it is difficult to find guidance since no one else has expertise in your field.
This team structure is highly effective because you have experts assigned to individual teams for unhindered reporting and analysis and another centralized team that may be working on a data lake.
We have already talked about the significance of data, but to utilize this information, you need people with special skills. Let's discuss some skills to look for when creating or expanding a data engineering team.
If your organization deals with unstructured data, then the candidate must be skilled in some NoSQL database system such as MongoDB.
Furthermore, for ‘Big Data’ requirements such as data lakes, candidates need to have experience with Apache Hadoop or any other big data tools available from cloud service providers such as Azure Data Lake.
These soft skills usually go underappreciated even though they are imperative to a data engineer's work.
Data engineers lay down the foundation of the entire data team. Their purpose is to structure the data and develop pipelines for effective data management. These pipelines allow easy data movement in and out of the system.
Data engineers need a diverse skill set to perform their job effectively; this is why making a team of data experts is tricky. You need to look for candidates with the technical and analytical abilities to process the data and good communication skills to minimize delays in work.
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