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Data Engineer vs. Machine Learning Engineer

How to Choose Between a Data Engineer and a Machine Learning Engineer

Understand the key differences between a data engineer vs machine learning engineer and how to choose the right hire for your business goals.

How to Choose Between a Data Engineer and a Machine Learning Engineer

Outline

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8
 MINUTE READ
What Is a Data Engineer?
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What Is a Machine Learning Engineer?
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Data Engineer vs. Machine Learning Engineer: Key Differences Explained
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How to Know If Your Business Needs a Data Engineer or a Machine Learning Engineer
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Final Thoughts
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Key Takeaways

  1. Data engineers build and manage the infrastructure that provides clean, organized, and accessible data across your organization.
  2. Machine learning engineers use structured data to develop, deploy, and maintain predictive models and intelligent systems.
  3. Choosing between the two depends on your current data maturity, project goals, and organizational needs for either infrastructure or intelligence.

Data engineers and machine learning engineers often get lumped together. It’s like two siblings who keep getting mistaken for each other at family gatherings. Sure, they both hang out with data all day, but their actual jobs couldn’t be more different. With companies scrambling to become data driven, understanding exactly who does what is more crucial than ever.

With so much overlap in data roles, deciding whether your business needs a data engineer or a machine learning engineer isn't always straightforward. Luckily, you've found your way here. 

In this article, you'll get a clear, jargon-free breakdown of these two critical roles, their day-to-day tasks, and most importantly, guidance on which one you actually need. By the end, you'll have actionable insights to confidently choose the right hire for your team.

What Is a Data Engineer?

Data engineers are responsible for building the systems that allow organizations to collect, store, and organize large volumes of data. Their primary focus is on the data infrastructure itself, developing and maintaining reliable, effective data pipelines that move data from source systems (like apps or sensors) into storage systems such as data warehouses or lakes.

A big part of their day revolves around managing ETL processes—ETL being short for extracting, transforming, and loading data. Essentially, they take raw, chaotic data and turn it into clean, structured information that's easy to analyze. They’re also responsible for overseeing databases and cloud platforms like AWS, Azure, and Google Cloud, making sure that your infrastructure remains scalable and secure as your business grows.

They also manage database architecture, implement data quality checks, and monitor data workflows to catch bottlenecks and inconsistencies. Their job is critical for providing downstream teams with accurate, timely, and well-structured data to work with.

To perform well in this role, data engineers need strong skills in SQL, coding experience in Python or Scala, and a solid understanding of distributed systems. 

What Is a Machine Learning Engineer?

Machine learning (ML) engineers are the architects behind the magic of intelligent software. When Netflix recommends exactly what you want to binge-watch next or your bank app detects fraud in seconds, it’s thanks to these engineers

They build, deploy, and continually improve machine learning models, bridging the important gap between data science (developing models) and software engineering (putting models into action).

Their daily tasks revolve around training new ML models, refining existing ones, and deploying these models into user-friendly applications. ML engineers constantly monitor how well their algorithms perform, making necessary adjustments to enhance accuracy and reliability. This iterative process means that predictions and recommendations remain precise and useful.

To successfully handle these tasks, ML engineers are typically skilled in Python and familiar with frameworks like TensorFlow or PyTorch. They also have a solid understanding of data science fundamentals.

Display of the word “DATA” symbolizing some shared roles of data and machine learning engineers

Data Engineer vs. Machine Learning Engineer: Key Differences Explained

When you’re hiring for a technical data role, titles can start to blur. Data engineers and machine learning engineers often work on the same teams and interact with the same datasets. But their responsibilities, skills, and even the value they bring to a project are very different. 

If you’re not sure which one your business needs (or whether you might need both), this breakdown will help clear things up.

Job focus, tech stack, and responsibilities

Data engineers focus on building the foundation, with their role deeply tied to infrastructure. 

Without them, there would be no reliable data for analytics or machine learning in the first place. They handle everything that comes before the data is ready to be used. This includes designing the architecture, building the pipelines, and making sure data flows where it needs to. Their work sets the stage for any meaningful analysis or automation that follows.

Machine learning engineers come in once that foundation is solid. They focus on using clean, structured data to develop intelligent systems that can predict, classify, or automate. Their role is less about moving data and more about making sense of it in real time. Here, they turn information into outcomes that drive products and business decisions.

Where data engineers are infrastructure-first, ML engineers are model-first. One role is concerned with storage and structure, and the other with behavior and prediction. They often collaborate closely, but their responsibilities and deliverables are distinct.

Both roles require strong programming skills (typically in Python), but they branch off from there. Data engineers work more with SQL, distributed systems, and tools like Apache Spark or Airflow. They also need to be fluent in cloud platforms like AWS, Azure, or GCP. 

Machine learning engineers go deeper into statistics, data science, and frameworks like TensorFlow or PyTorch. They also work with containerization and orchestration tools to deploy models at scale. A solid understanding of MLOps (machine learning operations) is becoming increasingly important.

If your team wants to build skills in intelligent systems, there are many online courses on ML available—from basic supervised learning to advanced AI deployment.

Here’s a side-by-side comparison to make the differences more tangible:

Comparison table of the differences between data engineers and ML engineers

Salary and market demand

Compensation for these two types of engineers reflects their specialized skills as well as their market demand.

Within the US, a data engineer typically earns between $87,000 and $177,000, depending on seniority and region. According to our data roles salary guide, hiring from Latin America can bring those costs down significantly, with regional averages ranging from $42,000 to $84,000.

Machine learning engineers, however, are in another league entirely when it comes to cost and demand. Between 2023 and 2024, the average US salary jumped by $35,000. Today, ML engineers command between $146,000 and $191,400 in the US

That number is expected to rise, especially with demand projected to grow by 26% over the next decade, according to the US Bureau of Labor Statistics. 

Companies looking to reduce costs without compromising on skill are increasingly turning to nearshore hiring. ML engineers in Latin America earn on average between $45,600 and $100,800.

Data storage servers often managed by data engineers

How to Know If Your Business Needs a Data Engineer or a Machine Learning Engineer

Choosing between a data engineer and a machine learning engineer comes down to your business goals and how far along you are with your data infrastructure.

You likely need a data engineer if:

  • You're collecting more data than you know what to do with, but it's scattered across tools or systems.
  • Your team is still manually updating reports or dashboards.
  • You’re moving data into a warehouse or planning a cloud migration.
  • You want to build reliable pipelines that make clean, structured data available for analysis.
  • You’re figuring out which type of data engineer can best support your use case.

You likely need a machine learning engineer if:

  • Your data is clean, centralized, and ready to be used in more advanced ways.
  • You're working on features like recommendation engines, fraud detection, or demand forecasting.
  • Your data science team has built models, but no one has the engineering chops to deploy and monitor them.
  • You want to turn analytics into automation or intelligence that improves customer experience or business performance.

In many growing teams, both roles become necessary. Data engineers make sure the right data is available in the right format. Machine learning engineers use that data to solve complex problems and build real-world applications. 

If you're scaling and want to use data more strategically, hiring both roles (either in sequence or together) can help you get there faster.

Final Thoughts

Data engineers and machine learning engineers might work with the same data, but they’re solving very different problems. One is focused on building the engine. The other is fine-tuning how it performs. The clearer you are on your goals, the easier it is to figure out who you need. 

And if you're trying to avoid sky-high US salaries while still hiring great talent? That’s where Near can help. We connect businesses with top talent—including experienced data experts—across Latin America. That way, you get the skills you need without stretching your budget.

Ready to move forward? Here's how to hire a remote machine learning engineer or a remote data engineer—depending on what your team needs next.

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