Understanding Types of Data Engineering Roles

Key Takeaways

Each data engineering role specializes in different aspects of data management, so identifying your business’s specific data needs is key to hiring the best fit.
Clearing up misconceptions about data engineers—like confusing their role with that of a data analyst—helps businesses set proper expectations.
Finding the right data engineer requires defining your data needs, assessing data complexity, identifying data challenges, choosing the right hiring approach, and evaluating technical and soft skills.

You already know your business requires a data engineer—but which type? Data engineers specialize in different areas, and hiring the right one depends on what you’re trying to achieve. Hiring the wrong fit can lead to inefficiencies and wasted resources.

In this guide, we’ll explore the many types of data engineering roles, clear up common misconceptions, and help you find the best fit for your needs.

Different Types of Data Engineers

Data engineering is one of the decade’s fastest-growing jobs, and that’s because business data has become more important than ever.

Data engineers play an important role in building and maintaining the systems that make data accessible and usable—but they also take on other tasks depending on their specific role.

Here’s a look at some common types of data engineering roles and what they do.

Data pipeline engineer: Builds and maintains systems that move data from various sources to storage or processing platforms. They work with Extract, transform, and load (ETL) frameworks—or ELT (Extract, Load, Transform) in cloud-based architectures—and real-time tools like Apache Kafka and Apache Airflow.
Data warehouse engineer: Designs and optimizes structured data storage solutions like Snowflake, Redshift, and BigQuery, focusing on organizing data for fast retrieval.
Data lake engineer: Manages large-scale raw data storage systems like AWS S3, Azure Data Lake, and Hadoop. They handle both structured and unstructured data.
Data infrastructure engineer: Builds and maintains the foundational architecture for data storage and processing. Their tools include cloud platforms, Kubernetes, and Terraform.
Data quality engineer: Maintains data accuracy and consistency by developing validation processes and automated testing frameworks.
Data DevOps engineer: Bridges data engineering and operations by automating deployments, monitoring pipelines, and optimizing workflows through CI/CD tools and infrastructure automation.
Machine learning (ML) data engineer: Prepares and optimizes data for ML applications, collaborating with data scientists and using ML frameworks like TensorFlow and PyTorch. (Note: This is a different role from that of a machine learning engineer.)
Security data engineer: Implements encryption, access controls, and compliance measures for regulations like GDPR and HIPAA to protect sensitive business information.

Two wowen discussing types of data engineering roles.

Common Misconceptions About Data Engineers

Since their work happens behind the scenes, there’s a lot of confusion about what data engineers actually do.

Many businesses know they need one but misunderstand their role or assume they can rely on other tech professionals to handle data engineering tasks.

Here are some of the most common misconceptions about data engineers.

A data engineer is just a fancy term for a data analyst

Data engineers and data analysts both work with data, but their roles are very different.

Data engineer: Builds and maintains the infrastructure that collects, stores, and processes data.
Data analyst: Focuses on interpreting and visualizing the data for business insights.

Without a data engineer, analysts would have to spend time cleaning and organizing data instead of focusing on analysis.

Businesses that confuse the two may end up with an overwhelmed analyst or a data engineer forced into a role they aren’t suited for.

Data engineers don’t need to understand business goals

Since data engineers work with technical systems, it’s easy to think they don’t need to grasp the broader business strategy. However, good data engineering relies on understanding business objectives.

With employee engagement sinking to a 10-year low, excluding departments like IT from strategic discussions can disconnect them from the company’s mission. Be sure to involve them so their solutions actively support your business and contribute to individual and business success.

Any software engineer can do data engineering

Although both software engineers and data engineers write code, their areas of expertise differ.

Software engineers: Focus on developing applications and user-facing features.
Data engineers: Work with data architecture, pipelines, and storage systems.

A software engineer might also be familiar with Python or SQL, but that doesn’t mean they have the expertise to build reliable, scalable data infrastructure.

All data engineers are full-stack experts

While some data engineers have broader expertise than others, many only excel at specific tasks.

Expecting your hired data engineer to handle everything from infrastructure to analytics to AI sets unrealistic expectations, which leads to workplace burnout and an unhealthy working relationship.

Instead, businesses should focus on hiring an engineer whose skills align with their specific data needs right from the start, rather than searching for “full-stack” experts who are few and far between.

Cloud services have replaced data engineers

Cloud platforms like AWS, Google Cloud, and Azure have simplified data infrastructure management, but they haven’t eliminated the need for data engineers.

A cloud data engineer ensures that cloud services integrate properly with a company’s existing data workflows. Without a data engineer, businesses may end up paying for inefficient cloud solutions or struggling with poor configuration.

There are also specialized data engineers, such as Azure data engineers or AWS data engineers, who focus on specific cloud platforms.

Data engineering is only about big data

Over half of SMBs lack the knowledge and experience to use data effectively regardless of the size of their datasets. Many feel overwhelmed by data management or simply don’t know how to maximize it.

Data engineering practices apply to businesses of all sizes, helping to clean, structure, and manage data efficiently. A well-structured data pipeline improves scalability, security, and data quality—even for businesses that don’t handle billions of records.

Two people discussing different types of data engineering roles

How to Find the Right Data Engineer for Your Specific Needs

Whether you need to optimize reporting, scale your infrastructure, or transition to the cloud, the ideal hire depends on your company’s unique requirements. Here’s how to hire the right person for the job.

1. Define your data needs and goals

Before hiring, clarify what you want to achieve with your data. Are you looking to improve reporting, migrate to the cloud, or handle large-scale analytics?

Understanding your objectives helps you pinpoint the necessary skills and experience. A business focused on real-time data processing, for instance, will need a skill set different from that of one that primarily deals with structured reports.

2. Assess your company’s data complexity

The complexity of your data operations determines the level of specialization required. A startup with relatively simple databases doesn’t need the same expertise as a multinational handling real-time streaming.

Here are a few examples of gauging based on complexity:

Small to medium datasets: A generalist or database-focused engineer is likely sufficient.
Big data (terabytes/petabytes, real-time streaming, or ML): You’ll need a specialized engineer with expertise in big data tools like Hadoop, Spark, or Kafka.

Knowing your data complexity ensures you don’t over-hire for a simple job or under-hire for a challenging one.

3. Identify data challenges

Consider the specific problems your company is facing. Different challenges require different types of data engineers.

Consider these examples:

Do you need better-organized databases for reporting and analysis?
A database/data warehouse engineer is the right choice.
Is your company moving to or scaling into the cloud?
A cloud data engineer is what you need.

Understanding these challenges narrows your search further.

4. Choose the best hiring approach

There are multiple ways to find a data engineer, and the most effective approach depends on your budget, workload, and long-term needs.

These include:

Full-time in-office: Ideal for ongoing, complex data projects done on-site. A data center engineer, for instance, is only suitable for an in-house role managing on-premises infrastructure.
Remote hire: Saves on overhead and expands your talent pool to other markets. Hiring a remote data engineer is great for cloud-based projects where location doesn’t impact performance.
Freelancer: A good choice for short-term or specialized tasks like building a specific ETL pipeline or integrating a new data source.
Outsourcing: Provides expertise without the need for a full-time hire, making it useful for companies that need support but lack in-house data teams.

5. Check technical skills and experience

Once you’ve started considering candidates, verify their technical expertise.

Some ways to do this include:

Reviewing their experience with specific tools like SQL, Azure, and Apache Airflow
Assessing past projects related to your industry or business needs
Administering a technical test focused on real-world scenarios, such as designing a scalable data pipeline

6. Evaluate problem-solving and communication skills

When assessing data engineer skills, soft skills matter just as much as technical ability. A data engineer must work with analysts, developers, and executives, making communication crucial.

However, assessing these skills isn’t always straightforward since they’re not as easy to measure as technical expertise.

These are some ways to do so:

Asking candidates to describe past challenges, their approaches, and the outcomes
Providing real-world data issues and asking them to walk through their thought process
Setting up a scenario where they need to communicate with a non-technical stakeholder or collaborate with a team on a mock project

If you’re not confident about the hiring process, working with a specialist recruiter can always help. They can connect you with pre-vetted data engineers who match your specific needs, saving time and increasing your chances of finding the right fit.

Final Thoughts

Finding the right data engineer comes down to understanding your business’s specific data challenges and goals. Whether you need someone to build robust pipelines, optimize your data warehouse, or enable machine learning capabilities, the key is defining your requirements upfront.

Don’t waste time with a general search for a “data engineer” when what you really need is someone with specialized expertise in cloud infrastructure or security compliance.

With rising costs of living, increasing demand, and a limited local talent pool in the US, hiring offshore in regions where the cost of living is less than in the US can be a more cost-effective solution to finding the data engineer you need.

Read our guide on why you should hire a data engineer from Latin America to find out how to save 30–70% compared to US market rates without compromising on quality.