What Is Data Engineering? A Complete Guide for Data Engineers and Data Science Teams

Not many people can accurately describe what data engineers do.

Data drives the operations of businesses, small and large. Businesses use data to provide answers to relevant inquiries that range from consumer interest to product viability.

That means that you run into major issues when your data is fragmented across systems, inconsistent, and difficult to access in a usable form.

Leadership teams don’t have reliable reporting to work from, and your operational teams end up making blind or misinformed decisions.

Without a doubt, data is an important part of scaling your business and gaining valuable insights. This makes data engineering, where systems that collect, store, transform, and move data are designed and built, incredibly important.

Let’s take a deeper look at what data engineering is, what they do, and if you need one for your business, and even where you can find them.

At Trio, we specialize in financial technology. There is a lot of data involved in financial applications and ensuring that they run correctly, but also many regulations around how that data is handled.

Our expert developers have the experience to deal with data effectively, allowing them to power fraud detection, automated underwriting, and more, without sacrificing regulatory compliance.

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What Is Data Engineering?

Data engineering is the practice of designing and building systems that collect, store, transform, and move data so that it can be used for analysis, reporting, machine learning, and AI.

Sometimes called information engineering, it is essentially a software approach to developing information systems.

To be clear, data engineering encompasses sourcing, transforming, and managing raw data from various systems.

Raw data tends to be quite messy. It usually arrives in inconsistent formats, from disparate sources, with missing values and structural inconsistencies that make it useless for analysis in its original state.

Data engineers are able to transform raw data into usable, reliable datasets through a combination of pipeline design, data modeling, and infrastructure management.

This process ensures that data is useful and accessible.

Building the data infrastructure that an organization relies on, the pipelines, warehouses, lakes, and transformation layers, represents the core of what data engineers do.

Similarly, data engineering relies on special mechanisms to apply found data to real-world scenarios, usually designing and monitoring sophisticated data processing systems to that effect.

Why Is Data Engineering Important?

Data engineering is important because it allows businesses to optimize data for usability. For example, engineering plays a large role in the following pursuits:

• Finding the best practices for refining your software development life cycle
• Maintaining data quality and protecting your business from data security vulnerabilities and cyberattacks
• Increasing your understanding of business domain knowledge
• Bringing raw data together into one place via data integration tools
• Making data accessible to data scientists, data analysts, and business intelligence teams who rely on data to generate insights

Whether business teams are dealing with sales data pipelines or analyzing their lead life cycles, data is present every step of the way.

Over the years, technological innovation has made data more important than ever before, with innovations including things like cloud technology, open-source projects, and the growth of data at scale.

The Difference Between Data Engineering and Data Science

Understanding the difference between data engineering and data science is important for anyone building or joining a data team, since the two roles are frequently confused but serve fundamentally different functions.

Data engineers ensure that this data is ready for data science teams in the first place.

Data engineers build systems; data scientists and analysts use them.

One of the most crucial aspects of data engineering is optimizing big data, which refers to the processes that take place in order to handle overtly complex or large sets of data.

In 2017, technology-based research company Gartner determined that between 60% and 85% of big data projects fail.

This is largely due to unreliable data infrastructure and data quality issues. Combined with the newfound digital transformation that many companies in the modern era find inevitable, quality data engineering is more important than ever.

We’ve noticed an increased trend where machine learning engineers can play the role of both data scientists and data engineers. Advanced data engineers sometimes do the work and fulfill the role of machine learning engineers.

What Is the Role of a Data Engineer?

Data engineers work across the full data lifecycle, from ingesting raw data from multiple sources and disparate systems, through transformation and data quality validation, to storage and delivery to downstream consumers.

There are several different engineering roles that fall under data engineering. Here's a quick breakdown:

Generalist Data Engineers

A generalist data engineer typically works on small teams, performing end-to-end data pipeline work and data collection.

Those in the generalist role have many skills, more so than other types of data engineers, but have less familiarity with the system data architecture.

Since small teams do not tend to have many users, generalists worry less about large-scale assignments and maintain a fully comprehensive role.

Pipeline-Centric Data Engineers

Mid-sized and larger companies are more likely to use pipeline-centric data engineers. For reference, a data pipeline is a data workflow that consolidates raw data from disparate sources.

A pipeline-centric data engineer will work across distributed systems on complicated data projects.

Their primary concern is making sure data flows reliably between systems. This includes the entire process of moving from source systems through transformation layers to data warehouses or data lakes, with consistent data quality at each stage.

Database-Centric Data Engineers

Large companies rely on database-centric data engineers to work on data that is distributed across several databases.

Database-centric engineers focus entirely on analytics databases.

This means they work closely with data scientists and business intelligence analysts, working across multiple data storage solutions, including data warehouses, and developing table schemas.

The Data Lifecycle and Modern Data Infrastructure

The data lifecycle describes the journey that data takes from creation or collection through to analysis and eventual archival or deletion.

As we have already mentioned above, data engineers are responsible for the infrastructure that supports every stage of this lifecycle.

The typical data lifecycle in a modern data infrastructure looks like this:

1. Ingestion: raw data is collected from multiple sources, like application databases, APIs, event streams, third-party platforms, and file uploads. Data engineers build the pipelines that move data from source systems into the data platform reliably.
2. Storage: ingested data lands in a data lake for raw storage or goes directly into a data warehouse for structured access. Cloud-native data warehouses like Snowflake, BigQuery, and Redshift have become the standard data storage solutions for most modern data teams.
3. Transformation: raw data is cleaned, validated, deduplicated, and modeled into structures that data analysts and data scientists can query without encountering data quality issues. dbt (data build tool) has become the dominant transformation layer in the modern data stack.
4. Serving: transformed data is made available to data scientists, data analysts, business intelligence tools, machine learning models, and AI applications through well-defined interfaces.
5. Monitoring and governance: data engineers maintain data quality checks, lineage tracking, and access controls across the pipeline. Data quality and governance represent an increasingly critical responsibility as organizations scale their data infrastructure.

The modern data stack has made some of this work a lot easier, but at the same time, it has complicated other parts of it.

Cloud-native tooling, for example, has lowered the barrier to building data pipelines. But the proliferation of data from multiple sources has raised the complexity of keeping data clean, consistent, and trustworthy at scale.

Tools and Technologies Data Engineers Use

Data engineers are technically software engineers, but traditional programming skills hardly scratch the surface of what data engineers are capable of.

Here's a summary of the data tools and technologies data engineers must be familiar with to do their job.

ETL Tools

ETL stands for extract, transform, and load.

Data integration tools of this kind describe a category of technologies for the data integration process.

Low-code development platforms have largely taken the place of traditional ETL tools in the present day. But the ETL process, in general, is still paramount to data engineering.

Informatica and SAP Data Services are some of the more well-known data tools for this purpose.

More recently, cloud-native alternatives like Fivetran, Airbyte, and Stitch have become common in data engineering for managed data integration pipelines.

Programming Languages

Data engineering calls for a variety of programming languages, namely back-end languages, as well as query languages and specialized languages for statistical computing.

Python, Ruby, Java, and C# are some of the most popular programming languages for data engineering, alongside SQL and R. You will often see Python, R, and SQL being used together.

Python is a general-purpose programming language that is easy to use with an extensive library. This makes it suitable for ETL tasks because the language is flexible and powerful.

Python has also become the primary language for building data pipelines in the modern data stack, with libraries like Pandas, PySpark, and SQLAlchemy seeing widespread use.

Then there is the structured query language (SQL), which our developers have also used for performing ETL tasks. It is the standard language for querying relational databases, which, unsurprisingly, is a big part of data engineering.

R is the go-to programming language and software environment for statistical computing. It's a favorite amongst statisticians and those working in data mining.

APIs

Application programming interfaces (APIs) are essentially a prerequisite for dealing with anything related to data integration.

APIs are integral to every software engineering project since they provide a link between applications and transport any information that needs to move between the two securely.

Data engineering relies on REST APIs, especially. REST, or representational state transfer, APIs can communicate over HTTP, making them a great asset for any web-based data tool. 

Data engineers use APIs both to ingest data from external sources and to expose transformed data to downstream consumers.

Data Warehouses and Data Lakes

We have already mentioned that data warehouses and data lakes refer to large, complex datasets that organizations store for business intelligence.

Data lake storage holds raw data in its original format, while a data warehouse stores structured, processed data optimized for querying and analysis.

In business-driven information engineering, business analysts manage these data storage datasets through computer clusters. This network of computers helps to better solve problems.

Spark and Hadoop are two well-known distributed data processing frameworks. The use of these frameworks is to prepare and process massive amounts of data.

Apache Spark in particular has become the dominant distributed data processing framework for large-scale data engineering, handling batch and streaming data workloads across cloud environments.

Data Quality, Data Security, and Data Management

Data quality and governance are one of the fastest-growing responsibilities within data engineering, as users become more educated on the topic and start to demand more and more transparency.

As organizations rely on data for AI training, regulatory reporting, and real-time decision-making, the consequences of poor data quality have become more significant.

Data engineers are increasingly responsible for:

• Designing data quality checks that run automatically within data pipelines.
• Tracking data lineage, so teams understand where data came from and how it was transformed.
• Implementing data security controls, including access management, encryption at rest, and audit logging.
• Ensuring compliance with data privacy regulations that govern how personal data is stored and processed.
• Managing data across its full lifecycle, including archival and deletion policies

To do this work effectively, they need to work alongside data governance, legal, and compliance teams to ensure the organizational policies are implementable in practice, and can scale rapidly as needed.

Career in Data Engineering: How to Become a Data Engineer

A bachelor's degree in computer science or a related discipline provides the strongest foundation if you are looking at a career in data engineering, as it offers a structured way to cover the programming, systems, and database concepts you need.

That said, many working data engineers entered the field through data analysis or database administration roles rather than a traditional computer science degree, and found very similar success.

To pursue a career in data engineering, a lot of our developers have learned the required skills in roughly this order:

1. SQL and relational databases
2. A general-purpose programming language, most commonly Python
3. Data pipeline design and ETL concepts
4. Cloud platforms (AWS, GCP, or Azure) and their native data storage solutions
5. Data warehouse tools (Snowflake, BigQuery, or Redshift)
6. Distributed data processing frameworks like Apache Spark
7. Data orchestration tools like Apache Airflow or Prefect
8. Data quality frameworks and data modeling best practices

If you are considering a career in data science, you’re going to need stronger statistical and machine learning foundations.

Demand for Data Engineers in 2026

Demand for data engineers has grown a great deal in recent years.

From what we have seen, this is largely driven by the expansion of data-dependent business operations, the rise of AI applications that require clean and structured training data, and the increasing complexity of modern data infrastructure.

Bureau of Labor Statistics projections for data-related roles show growth rates significantly above the average for all occupations.

All of this demand has made developers with this skillset quite expensive.

Senior data engineers in the US typically earn $150,000-$200,000 in total compensation, with particularly strong demand in fintech, healthcare technology, and e-commerce.

These developers are also becoming more difficult to hire, since you are competing with major companies that can offer competitive packages.

Nearshore data engineers from LATAM, working in US time zones, represent an increasingly common solution for companies that need to build data infrastructure fast without the delays and cost of US senior hiring.

At Trio, for example, we offer fintech data engineering experts for anywhere from $40-$90 depending on your specific requirements. This can be as much as 60% less than that of a US-based developer with a similar skillset, without a sacrifice in quality.

Why Data Engineering Is Critical For Business

Data engineers build systems and data pipelines that prepare and process raw data for future analysis.

It goes without saying that raw data isn't useful unless it is readable. Without data engineers to build the infrastructure that transforms raw data into structured, reliable datasets, data scientists and analysts have nothing meaningful to work with.

At Trio, we understand the importance of data engineering for business scalability. That's why we have highly qualified data engineers on our team equipped to take your organization to the next level.

Our developers are also experienced in fields like fintech, where data handling is highly governed.

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