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:
- Targeted advertisement campaigns
- Application improvements via user reviews
- Predictive analysis
- Selling data to other organizations
- Data-driven customer and business insights and analytics
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.
Who are the Rockstars of a Data Team?
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.
- Chief Data Scientist/Officer (CDS/CDO): These are the front runners of the data engineering team–the real decision-makers and architects of an organization’s data products.
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.
- Data Engineers: While a CDO is an architect, engineers are the workforce. Data engineers act upon the pathway set by the CDS/CDO and structure data in a way other teams can utilize. They construct and maintain pipelines for dumping collected data into the database in an optimized and structured manner.
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 Analysts: Data analysts have multiple responsibilities within the data engineering team. A database is their playground where they will interact with this data and perform all sorts of processing. Some of their significant tasks include cleaning the data, using tools for statistical modeling, using visualization tools for converting data into a presentable form, and preparing reports for higher management.
- Data Scientists: The terms ‘Data Scientist’ and ‘Machine Learning Engineer’ are often used interchangeably. They are responsible for applying technical, statistical, and predictive analyses to data, such as classification, regression, and clustering. Additionally, they are also responsible for data collection and cleaning, so their expertise overlaps with data analysts.
- Database Administrators (DBAs): Database administrators are responsible for monitoring the health of all the databases within an organization. They also optimize queries written by other members of the data engineering team to ensure that the application does not face any congestion.
Roles & Responsibilities of a Data Engineering Team
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.
- Database Design: Data engineers must design database infrastructure optimally so that all-important linkages and relationships between data are maintained while keeping space complexity to a minimum.
- Creating ETL Pipelines: ETL stands for extract, transform and load. Every data engineer must know how to build pipelines to carry out these tasks. They are required to write SQL queries that allow data migration between sources. Examples of such migration are:
- Migrating data from external sources into your system.
- Migrating data from your organization to an external source.
- Migrating data in between your local databases for use by different teams.
- Creating Data Warehouses and Lakes: Organizations with large-scale web applications require their data to be stored in a centralized repository called a warehouse or a data lake.
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.
Centralized vs. Decentralized Data Engineering Teams – Maybe A Bit of Both!
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.
- Centralized: All your organization's data teams (analysts, engineers, DBAs, etc.) work as a single central team. They all work under the same management, so if any other teams need to make a request, they contact the manager.
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.
- Decentralized: Each team within an organization (finance, sales, software development, etc.) is assigned a separate data wizard. This assignment depends on the teams' requirements, i.e., some teams may require a data engineer, and others may need a data analyst. Some may even need multiple data professionals.
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.
- Hybrid Approach: Sometimes, you may need a centralized and decentralized data team for smooth operation. Having both groups can help eliminate the drawbacks of the other.
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.
Skills Required for Scaling Data Engineering Teams
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.
- Technical skills: A sound data engineer must have hands-on experience with data querying languages such as SQL and some general-purpose programming languages such as Python and JavaScript.
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.
- Soft Skills: A data engineer's work requires communicating with other teams and clients. So some of the soft skills required are:
- Ability to gather requirements
- Emotional intelligence
- Good analytical skills
- Strong communication skills within the team
- Effective reporting to executives and other stakeholders
These soft skills usually go underappreciated even though they are imperative to a data engineer's work.
Data Engineering Team – The Key to Solving Your Data Mysteries
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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