Data Engineer

TL;DR

• Builds and maintains data systems and pipelines that move and prepare data from multiple sources.
• Consolidates data into central repositories (for example, a data warehouse or data lake) so organizations can access and analyze large volumes of data.
• Enables organizations to gain insights and make informed decisions from consolidated data.

Definition

A data engineer is a professional who focuses on the design, development, and maintenance of data systems and pipelines that extract, transform, and load data from various sources into a central repository, such as a data warehouse or a data lake.

Explanation

Data engineers create and manage the infrastructure that lets organizations collect, store, and process data. Their work ensures data from databases, files, APIs, and other sources is transformed into a consistent format and loaded into a central location. By organizing and securing data repositories, data engineers enable downstream analysis and decision-making based on large volumes of data.

Examples

ETL pipeline

An ETL (extract, transform, load) pipeline extracts data from various sources (such as databases, files, and APIs), transforms it into a consistent format, and loads it into a central repository. For example, a data engineer may create an ETL pipeline to combine customer data from different systems, such as an online store, a customer relationship management (CRM) system, and a social media platform. This provides a comprehensive view of customers that can be used for analysis and marketing efforts.

Data lake

A data lake is a large repository of raw data stored in its original format. It allows organizations to store and access data from various sources (such as social media, sensors, and logs) without prior preparation or transformation. Data engineers design and implement data lakes and ensure the data is organized and secure. For example, a data engineer may create a data lake to store and analyze customer data from web logs, purchase history, and social media interactions to gain insights on customer behavior, preferences, and trends.

Use cases

• Consolidating customer data for analysis and marketing efforts.
• Storing diverse raw data to enable analysis of customer behavior, preferences, and trends.
• Enabling organizations to access and analyze large volumes of data to gain insights and make informed decisions.

Related terms

• ETL (extract, transform, load)
• Data warehouse
• Data lake
• CRM (customer relationship management)
• Data systems
• Data pipelines