What Does a Data Engineering Team do?

Data has become a central component of modern business strategy and decision-making. Data engineering teams must build tools capable of handling current demand while providing the tools and infrastructure to scale.

Data engineering teams design, build, and maintain data infrastructure platforms. They transform raw data into meaningful analytics and insights organizations use to design models, develop strategies, run analyses, and perform other data-related decision-making.

Data Team Roles & Responsibilities

There are three core roles to every data team:

• Data Engineer
• Data Analyst
• Data Scientist

What does a Data Engineer do?

Data collection starts with data engineers. They work with raw data from various sources to transform it into usable information the rest of the organization can interpret and analyze.

Data engineers are responsible for designing, building, and maintaining data infrastructure tools and platforms.

Average Data Engineer salary in the United States: $116,999.

What does a Data Analyst do?

As the name suggests, data analysts analyze data collected from engineers to derive insights to maintain data platform dashboards, generate reports, and data visualizations.

Analysts create four primary types of data analysis:

• Descriptive analysis: Uses statistical techniques to present data—answers the question, "What happened?"
• Diagnostic analysis: Identifies trend causes and correlations between data sets—answers the question, "Why did it happen?"
• Predictive analysis: Used to create models for future predictions about trends or events—answers the question, "Why might happen?"
• Prescriptive analysis: Uses multiple correlating data sets to predict possible outcomes and suggests the best course of action for the desired result—answers the question, "What should we do next?"

Average Data Analyst salary in the United States: $65,114.

What does a Data Scientist do?

Data scientists are responsible for writing algorithms and building tools that mine and organize data. They dive deep into data and analytics with advanced mathematics and programming techniques for large-scale analysis.

Some of the data scientist's tools include:

• Statistical modeling: Uses mathematical models and assumptions to make real-world predictions
• Machine learning: Uses sample data to make predictions and decisions without specific programming (commonly used for email filtering, speech recognition, and other programs where there are many dynamic factors)
• Artificial intelligence (AI): Advanced algorithms where data scientists set goals, and the system develops the best strategies to collect and analyze data

Check the role Data analyst here.

Average Data Scientist salary in the United States: $74,507.

Other Data Team Roles

These are some of the other roles you find in larger organizations managing significant volumes and data types.

• Business analyst: Usually work with marketing or finance departments to help answer data-related business questions. Business analysts bridge IT and business to facilitate efficient collaboration between engineers and the rest of the organization. Average Business Analyst salary in the United States: $74,430.
• Database administrator: Manages and evolves legacy data. They are primarily responsible for ensuring legacy data retains quality and integrity as it's migrated to modern systems. Average Database Administrator salary in the United States: $86,523.
• Data manager: Oversees the governance and operations for data teams. Average Data Manager salary in the United States: $75,277. Further up the chain in larger organizations, you get a Data Director and Chief Data Officer. In most cases, the data team will fall under IT leadership.

How to Structure a Data Team

Here are several scenarios for structuring a data team.

Centralized vs. Embedded Data Teams

Firstly, it's essential to understand the two types of data team structures.

• Centralized data team: Operates as a department of engineers, scientists, and analysts working together on data projects reporting to a data manager.
• Embedded data team: Data team members work in cross-functional teams, reporting to a leader for day-to-day responsibilities. The data manager still has control over data governance and operations.
• Hybrid data team: The hybrid model uses embedded data analysts or business analysts who work with a centralized data team.

A centralized data team works well for startups and small to medium-sized companies. This model will struggle with organizations relying heavily on data or processing significant volumes.

Hybrid and embedded data teams work well for medium to enterprise organizations, especially those using Agile project management. Embedded data teams look after projects and end-users, while the centralized team of engineers and analysts manages the organization's data storage and distribution across the company.

Hybrid and embedded data models come with challenges, consistency being a significant issue. To help scale while maintaining standards, consistency, and data collaboration, organizations adopt DataOps.

What is DataOps?

DataOps is a collection of tools, workflows, practices, and architectural patterns that allow data teams to innovate and scale while maintaining quality, consistency, and communication. In short, DataOps seeks to solve the challenges of scaling data operations in enterprise environments with embedded team members working on different projects and goals.

Data Teams for Startups & Small Businesses

For most organizations, a Data Analyst will be a valuable first hire. Analysts can organize existing data and develop systems for making it more accessible to the rest of the company.

As you scale, a Data Engineer can develop more efficient and robust systems for collecting and managing raw data. This frees the Data Analyst to focus on users to provide them with better ways of viewing and digesting data.

Scaling a Data Team

Data projects require a specific skill set. The key to building a data team is understanding your company's data and the skills necessary to scale your operations. Here are some core data skills:

• Databases (designing, building, managing)—Data Engineer
• Software development—Data Engineer/Data Analyst
• Machine learning/AI—Data Scientist
• Visualization—Data Analyst/Business Analyst
• Reporting & communication—Data Analyst/Business Analyst
• Management—Data Engineer/Analyst

Final Notes on How to Structure a Data Engineering Team

Building a data team will depend on your data needs and strategy. You also have to consider your industry, location, legislation, and other factors that determine what positions you must fill and their priority.

Reaching data maturity is challenging and takes time. If you want to be a data-driven company, a strong analytics team is the foundation that aligns with your business goals is crucial. The most important thing to understand is the roles of data engineers, data analysts, and data scientists and what value they will add to your organization.