Think of data engineers as the architects and builders of the digital infrastructure required to handle data. While the previous section defined data engineering, here we look at what a data engineer actually does day-to-day. Their primary goal is to make high-quality data available, reliable, and easily accessible for others in the organization, such as data analysts, data scientists, and machine learning applications.

If data is the new oil, data engineers build the refineries, pipelines, and storage tanks. They design, construct, install, test, and maintain the systems that manage data flow from various sources to its final destinations.

Here are some of the common responsibilities and tasks performed by data engineers:

Designing and Building Data Pipelines

This is a central part of the job. Data engineers create automated processes, known as data pipelines, to move data from where it's generated (like application databases, user activity logs, or third-party APIs) to systems where it can be stored and analyzed (like data warehouses or data lakes). This involves figuring out how to extract data, how it needs to be cleaned or reshaped (transformed), and where and how to load it. You'll learn more about pipelines, including ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), in Chapter 3.

Managing Data Storage Systems

Data doesn't just magically appear where it's needed. Engineers select, set up, and manage various storage solutions based on the type of data, how quickly it needs to be accessed, and how it will be used. This includes working with traditional relational databases (like PostgreSQL or MySQL), NoSQL databases (like MongoDB or Cassandra), large-scale distributed storage like data warehouses (Snowflake, BigQuery, Redshift) and data lakes (often built on object storage like AWS S3 or Google Cloud Storage). Chapter 4 covers these storage options.

Ensuring Data Quality and Reliability

Raw data is often messy, incomplete, or inconsistent. Data engineers implement checks and balances within their pipelines to clean data, validate its accuracy, and ensure its integrity. They build monitoring and alerting systems to detect problems with data pipelines or storage systems, ensuring that the data users rely on is trustworthy and available when needed.

Setting up Data Processing Systems

Data often needs to be processed, aggregated, or summarized. Engineers work with different processing frameworks to handle computations on data. This might involve batch processing for large, periodic jobs (like generating a daily sales report) or stream processing for real-time data (like analyzing website clicks as they happen). Chapter 5 introduces these processing paradigms.

Optimizing and Maintaining Systems

Building a system is just the start. Data engineers continuously monitor the performance of data pipelines and storage systems, looking for bottlenecks or inefficiencies. They tune databases, optimize queries, refactor code, and scale infrastructure to handle growing data volumes and user demands, ensuring the systems remain performant and cost-effective.

Collaborating with Stakeholders

Data engineers don't work in isolation. They collaborate closely with data scientists to understand their data requirements for model building, with data analysts to provide data for reports and dashboards, and with software engineers to integrate data collection into applications. Understanding the needs of these different groups is fundamental to designing effective data solutions.

The following diagram illustrates where the data engineer typically fits within the flow of data in an organization:

Data engineers connect various data sources to data consumers by building and managing pipelines, storage, and processing systems.

In essence, a data engineer ensures that the rest of the data team has reliable and performant access to the data they need to derive insights, build models, or make decisions. They lay the groundwork that makes sophisticated data analysis and AI possible.