Data Engineer

What Is a Data Engineer?

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts.

In addition to making the lives of data scientists easier, working as a data engineer can give you the opportunity to make a tangible difference in a world where we’ll be producing 463 exabytes per day by 2025 [1]. That’s one and 18 zeros of bytes worth of data. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data

What does a data engineer do?

Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.

Listen to some practicing data engineers talk about what they do.

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts.

In addition to making the lives of data scientists easier, working as a data engineer can give you the opportunity to make a tangible difference in a world where we’ll be producing 463 exabytes per day by 2025 [1]. That’s one and 18 zeros of bytes worth of data. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data.

What does a data engineer do?

Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.

Listen to some practicing data engineers talk about what they do.

These are some common tasks you might perform when working with data:

• Acquire datasets that align with business needs
• Develop algorithms to transform data into useful, actionable information
• Build, test, and maintain database pipeline architectures
• Collaborate with management to understand company objectives
• Create new data validation methods and data analysis tools
• Ensure compliance with data governance and security policies

Working at smaller companies often means taking on a greater variety of data-related tasks in a generalist role. Some bigger companies have data engineers dedicated to building data pipelines and others focused on managing data warehouses—both populating warehouses with data and creating table schemas to keep track of where data is stored.

The data engineer role

Data engineers focus on collecting and preparing data for use by data scientists and analysts. They take on three main roles as follows:

• Generalists. Data engineers with a general focus typically work on small teams, doing end-to-end data collection, intake and processing. They may have more skill than most data engineers, but less knowledge of systems architecture. A data scientist looking to become a data engineer would fit well into the generalist role.

A project a generalist data engineer might undertake for a small, metro-area food delivery service would be to create a dashboard that displays the number of deliveries made each day for the past month and forecasts the delivery volume for the following month.

• Pipeline-centric engineers. These data engineers typically work on a midsize data analytics team and more complicated data science projects across distributed systems. Midsize and large companies are more likely to need this role.

A regional food delivery company might undertake a pipeline-centric project to create a tool for data scientists and analysts to search metadata for information about deliveries. They might look at distance driven and drive time required for deliveries in the past month, then use that data in a predictive algorithm to see what it means for the company's future business.

• Database-centric engineers. These data engineers are tasked with implementing, maintaining and populating analytics databases. This role typically exists at larger companies where data is distributed across several databases. The engineers work with pipelines, tune databases for efficient analysis and create table schemas using extract, transform, load (ETL) methods. ETL is a process in which data is copied from several sources into a single destination system.

A database-centric project at a large, multistate or national food delivery service would be to design an analytics database. In addition to creating the database, the data engineer would write the code to get data from where it's collected in the main application database into the analytics database.