What’s the Difference Between a Data Engineer and a Machine Learning Engineer?
The main difference between a data engineer and a machine learning engineer is what they deliver. A data engineer builds the pipelines that collect, clean and store your data. A machine learning engineer uses that data to build models that run inside your product, and keeps them accurate once they’re live.
What does a data engineer do vs. a machine learning engineer?
A data engineer owns your data up to a clean, tested dataset. A machine learning engineer owns everything from that dataset to a working model in production.
| Data engineer | ML engineer | |
|---|---|---|
| Main job | Get clean, trusted data to everyone who needs it | Get models into production and keep them working |
| Builds | Ingestion pipelines, warehouses, batch and streaming jobs | Training pipelines, feature stores, model APIs, retraining jobs |
| Main output | Tested, documented datasets that land on schedule | Models serving predictions inside your product |
| Typical tools | SQL, Python, Spark, Airflow, dbt, Kafka, Snowflake or BigQuery | Python, PyTorch or TensorFlow, MLflow, Docker, SageMaker or Vertex AI |
| Measured on | Pipeline uptime, data freshness, data quality | Model accuracy in production, latency, cost per prediction |
| Math and statistics | Light | Heavy: statistics, algorithms, model evaluation |
The two roles work closely because one depends on the other. When a model in production starts making worse predictions, the first thing to check is usually whether something changed upstream in the data.
Who earns more, a data engineer or a machine learning engineer?
Machine learning engineers earn more, but how much more depends on where you hire. In the US the two roles pay almost the same. In Latin America, senior ML engineers earn about 40% more than senior data engineers.
| Salary per year | Data engineer | ML engineer |
|---|---|---|
| US average | $127,500 | $128,800 |
| LatAm, entry level (1–3 yrs) | $30K–$41K | $41K–$50K |
| LatAm, senior (6+ yrs) | $68K–$78K | $91K–$111K |
LatamCent salary estimates for remote roles with US companies in Colombia, Mexico, Brazil and Argentina, 2026. Ranges show the lowest and highest of the four countries.
Should you hire a data engineer or a machine learning engineer first?
Hire a data engineer first if your data isn’t reliable yet. Hire a machine learning engineer first if your data is in good shape and you need a model running in your product.
- Data engineer first if your data lives in a dozen tools and your dashboards don’t agree with each other. A model built on that data won’t be any more reliable than the data itself.
- ML engineer first if a data scientist already has a model working in a notebook and it now needs to run in production, retrain on a schedule and feed your product.
- Only budget for one? An ML engineer with strong pipeline experience can cover both early on. Split the work into two roles once your data volume grows.
Need an ML engineer who works well with your data team?
LatamCent hires pre-vetted machine learning engineers in Latin America who work US hours and cost far less than a US hire. You interview a shortlist and make the hire in 21 days or less.
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