What is data engineering?

Data engineering is the foundation of modern data work. As data volumes grow and business decisions become more reliant on real-time insights, the role of the data engineer has become critical to organizational success.

But what exactly is data engineering, and why does it matter?

In this guide, we’ll define data engineering, outline the key responsibilities of data engineers, and explore its growing impact across industries. We’ll also share how dbt helps modern data teams streamline engineering workflows and deliver trusted, analysis-ready data.

What is data engineering?

Data engineering is the practice of designing, building, and managing the infrastructure that enables efficient data collection, storage, transformation, and analysis. At its core, it’s about making raw data usable—so analytics engineers, data scientists, and business teams can turn it into insight.

Data engineers create and optimize the pipelines that move data from source systems into centralized platforms like cloud data warehouses. Their work ensures data is timely, accurate, and accessible.

It’s important to distinguish data engineering from data science. While data scientists analyze and interpret data to build models or answer questions, data engineers build the systems that make that work possible.

Think of data engineering as the backbone of the modern data stack. Without it, none of the higher-value data work can happen.

Data engineering vs. data science

Data engineering and data science are often mentioned together, but they serve distinct, complementary roles in the modern data team.

Data science is about deriving insights from data: building predictive models, uncovering patterns, and supporting business decisions through analysis.

Data engineering focuses on making that analysis possible: designing the systems and pipelines that ensure data is clean, structured, and ready for use.

Without solid infrastructure, even the best analysis can’t get where it needs to go.

Why is data engineering important?

Strong data engineering is the difference between having data and actually using it.

When data is fragmented, messy, or slow to access, teams waste time and make decisions based on incomplete information. A modern data engineering function ensures data is well-organized, governed, and available—so teams can focus on delivering insights.

By building scalable pipelines and standardizing data transformation, data engineers enable everything from operational dashboards to advanced machine learning.

The demand for data engineers is only growing

Data engineering is one of the fastest-growing roles in tech—and it’s no surprise why. Organizations are generating more data than ever. By 2027, global data creation is expected to hit 394 zettabytes per day.

As data volumes surge, so does the need for skilled professionals to manage, structure, and transform that information. The World Economic Forum lists data engineering as one of the top growth jobs through 2030. It’s a core function for any company that wants to be data-driven.

That demand comes with strong financial incentives. According to Glassdoor, average salaries for data engineers consistently reach six figures—reflecting both the importance and the scarcity of this expertise.

Key responsibilities of a data engineer

The role of a data engineer spans a wide range of technical and strategic responsibilities. At a high level, they ensure data flows smoothly from source systems to destinations where it can be analyzed and acted on. Core responsibilities include:

• Building and maintaining data pipelines: Data engineers design and manage the pipelines that move data from source systems (like databases, APIs, or streaming platforms) into centralized storage. These pipelines must be reliable, efficient, and scalable.
• Ensuring data quality: Clean, accurate data is non-negotiable. Data engineers build validation and cleaning processes to catch errors, handle anomalies, and maintain trust in downstream reporting and analytics.
• Data transformation: Raw data often needs significant reshaping before it’s ready for use. Engineers write transformation logic to normalize formats, combine datasets, apply business rules, and prepare data for analysis.
• Optimizing data storage: Choosing the right storage solution—whether a relational database, cloud warehouse, or NoSQL system—is critical. Data engineers design schemas and partitioning strategies that balance speed, cost, and scalability.
• Scaling infrastructure: As data volumes grow, infrastructure must scale with them. Data engineers automate workflows, optimize performance, and ensure the stack can handle increasing complexity without slowing down.

Data pipelines: the heart of data engineering

Data pipelines are the foundation of modern data engineering. These automated workflows move raw data from its source, apply necessary transformations, and load it into systems where it can be stored, analyzed, and acted on. Pipelines ensure that data flows continuously and reliably across the organization.

There are several types of data pipelines, each suited to different use cases:

• Batch processing pipelines: These pipelines process large volumes of data at scheduled intervals—for example, aggregating daily sales from a retail system every night.
• Streaming pipelines: Designed for real-time processing, these pipelines handle data as it arrives. Industries like finance rely on streaming pipelines for up-to-the-second decision-making.
• ETL (Extract, Transform, Load): In ETL workflows, data is extracted from source systems, transformed outside the warehouse, and then loaded into a destination. dbt helps simplify the transformation layer by enabling engineers to build modular, testable SQL logic.
• ELT (Extract, Load, Transform): A modern alternative, ELT workflows load raw data directly into the warehouse first, then apply transformations inside the warehouse. This approach is more scalable—and it’s where dbt excels. With dbt, teams can transform data using version-controlled SQL models, automated testing, and lineage documentation.

The data engineering lifecycle

Data engineering follows a structured lifecycle to ensure data is handled consistently from collection to analysis. Each stage plays a critical role in transforming raw inputs into trusted insights:

1. Data collection

Data engineers gather data from various sources—databases, APIs, web services, and streaming platforms. These inputs are often unstructured or semi-structured, requiring processing before they’re analytics-ready.

2. Data storage

Once collected, data needs to be stored in a way that supports scalability and accessibility. Depending on volume and use case, this could involve data lakes, cloud storage, or modern data warehouses.

3. Data transformation

After storage, raw data must be cleaned, standardized, and modeled for analysis. This includes filtering, formatting, and joining datasets. Tools like dbt enable engineers to automate this process with version-controlled, modular SQL logic—making transformations more transparent and reliable.

4. Data analysis

Once transformed, data is ready for use by analysts and data scientists. Data engineers continue to support this phase by maintaining infrastructure that enables fast, dependable querying.

5. Data governance

Data engineers implement governance policies to ensure data is secure, compliant, and discoverable. This includes managing access controls, tracking lineage, and enforcing standards around data quality and retention.

Common challenges in data engineering

Data engineers navigate a wide range of technical and organizational challenges. Each requires sharp problem-solving and a strong foundation in data infrastructure.

• Data sprawl and diverse sources. Engineers must integrate data from many sources—often in incompatible formats. Bringing this data together is essential to create a unified, trusted foundation for analysis.
• Ensuring consistency and quality. Reliable insights depend on clean, accurate data. Engineers build systems that validate and standardize data as it flows through pipelines, catching errors early and maintaining trust in downstream outputs.
• Scaling data infrastructure. As organizations grow, so does the volume and complexity of their data. Engineers must continuously optimize pipelines and storage to ensure performance, reduce cost, and support increasing user demands.
• Governance and security. Engineers are responsible for implementing access controls, tracking data lineage, and enforcing compliance. Strong governance safeguards sensitive data and supports auditability.
• Balancing batch and real-time needs. Many teams rely on a mix of real-time and scheduled data workflows. Engineers must design flexible systems that can support both modes, enabling timely insights without sacrificing efficiency.
• Automating complex workflows. Modern data pipelines often include multiple steps, dependencies, and stakeholders. Engineers use orchestration tools to automate these workflows, reducing manual effort and increasing reliability.
• Keeping pace with evolving tools. The data ecosystem evolves quickly. Engineers need to stay current with emerging frameworks, tools, and best practices to build scalable systems and support innovation.

The tools of the data engineer

Data engineers use a wide range of tools to build reliable, scalable data systems. Here are some of the most common:

• dbt: dbt is a SQL-based transformation tool that lets engineers build modular, tested, and version-controlled data pipelines directly in the data warehouse. It supports cloud platforms like Snowflake, BigQuery, Redshift, and Databricks, and brings software engineering best practices—like CI/CD and documentation—to analytics workflows.
• Apache Kafka: Kafka is a distributed event streaming platform used for ingesting and processing large volumes of real-time data. It’s commonly used for building high-throughput, fault-tolerant data pipelines.
• Apache Spark: Spark is a powerful processing engine for large-scale data workloads. It supports both batch and streaming data and is often used for machine learning, ETL, and analytics workflows requiring distributed computing.
• Apache Airflow: Airflow is a workflow orchestration platform that allows data engineers to programmatically author, schedule, and monitor complex pipelines. It’s commonly paired with dbt to coordinate jobs across the modern data stack.

Conclusion

Data engineering powers the modern data stack. It ensures organizations can collect, transform, and deliver reliable data at scale—fueling everything from operational reporting to advanced analytics.

By building robust pipelines, enforcing data quality, and streamlining transformation, data engineers lay the groundwork for trusted, actionable insights. And with modern tools like dbt, teams can bring software engineering practices—like version control, testing, and modular development—into the data workflow.

Whether you’re scaling a mature data platform or just starting out, a strong foundation in data engineering principles—and the right tools—will set your organization up for long-term success in a data-driven world.

The role of dbt in data engineering

dbt helps data engineers transform data directly in the warehouse using modular, SQL-based code. It brings software engineering best practices—like testing, version control, and documentation—into the analytics workflow.

With dbt, data teams can:

• Build modular transformations: Reuse logic across models to reduce duplication and make pipelines easier to manage.
• Collaborate through version control: Built-in Git workflows allow teams to review, test, and track changes before they go live.
• Scale confidently: dbt works natively with platforms like Snowflake, BigQuery, and Redshift to support large, complex data environments.

dbt makes it easier to manage ELT workflows and ensures your pipelines are reliable, governed, and built to scale.

Try dbt for free to see how it simplifies the way data engineers build and maintain trusted data pipelines.

FAQs about data engineering