What does a (Cloud) Data Engineer do versus a Machine Learning Engineer?

Data Engineers and Machine Learning Engineers make data available for:

• Analyses – read more here about how we used analysis to strengthen FrieslandCampina's competitive position.
• Dashboarding – read more here about how we used dashboards to promote data-driven work in crisis organisations.
• Machine Learning and AI applications – read more here about how predictive maintenance reduced complaints.

Traditional IT roles such as Architect, Cloud Engineer, Platform Engineer, and DevOps Engineer are now also used in the world of Data Engineering. Although we will not go further into all these roles in this blog, it is important to acknowledge that there are various niches in the field. Sometimes there is a lot of overlap between the roles, and the tasks can shift in focus over time. At the beginning of a project, the focus may be on architecture, which can later become a smaller role.

The Role of a Data Engineer

A Data Engineer focuses on designing, building, and maintaining scalable data infrastructures and pipelines. They integrate, process, and store large amounts of data from various sources. This is typically done on cloud platforms, and they use ETL/ELT processes to ensure that data is accessible and usable.

ETL (Extract-Transform-Load)

ETL is a process where data is first extracted from sources, then transformed into a suitable format, and finally loaded into a data warehouse.

ELT (Extract-Load-Transform)

ELT is a process where data is extracted and directly loaded into storage, and transformations are performed within the data storage environment.

Traditional vs. modern data processing methods

Traditionally, Data Engineers worked with ETL tools where data was transformed before it was stored. Nowadays, due to cheaper cloud storage, data is first stored and then transformed (ELT), which enables faster and more efficient data processing.

Cloud Platforms

In the Netherlands, the most commonly used cloud platforms are Microsoft (Azure), Google (Google Cloud Platform), and Amazon (Amazon Web Services). In this article, you can read about the benefits of a cloud data infrastructure and what a cloud migration looks like in practice.

The role of a Machine Learning Engineer

In recent years, a new role has emerged: the Machine Learning Engineer. This role focuses specifically on implementing and maintaining machine learning models within a production environment.

Responsibilities

• Model implementation and maintenance (MLOps): Implementing and operationalising ML models so that they are available for use in production environments. Read more about MLOps here.
• Tools and frameworks: Using tools such as Databricks, Azure ML Studio, and AWS SageMaker in combination with MLflow.
• Python packages: Using TensorFlow, PyTorch, scikit-learn, and Spark MLlib for model development.
• Testing: Not only testing the code through unit and integration tests but also testing the output of ML models to ensure that predictions in production are accurate.

Differences between a Data Engineer and a Machine Learning Engineer

A Data Engineer mainly focuses on setting up infrastructure and making data available. A Machine Learning Engineer consumes and processes data, focusing on training, validating, and optimising ML models in production environments.

Interested in working with us?

Are you interested in a dynamic role within data engineering? We invite you to apply for our Data Engineer vacancy, even if you are interested in the role of Machine Learning Engineer. Together, we can harness the power of data to generate valuable insights and develop innovative solutions.

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