Data Engineers vs. Machine Learning Engineers

In this age of rapid technological advancement, more and more businesses are growing and expanding online over the past decade. When everything’s online, that just means scores and scores of data. As your online business deals with a growing volume of data, it becomes essential to enlist the expertise of professionals and leverage technology. Ensuring that capable professionals handle your valuable data is crucial for effective processing, organization, and analysis. This is where the distinction between data engineers vs. machine learning engineers becomes crucial. As you navigate through the myriad of IT specializations, understanding this difference is vital for making informed decisions in hiring and enhancing the capabilities of your online business.

Data Engineers 

When thinking about data-related professions, the first thing that comes to mind for most people is being a data scientist. What makes data engineers different is their responsibility to deliver the infrastructure and framework to process the data that you have. Data scientists, on the other hand, create a model or algorithm that will help you use data in a more practical way such as automating your tasks. 

Think of data engineers as architects—business intelligence architects to be precise. Data engineers have a solid grip on business intelligence tools. They also manage the database they create to make sure that everything is working like a well-oiled machine. This ensures that you and anyone with the appropriate authority can easily access the desired data or information contained in the database.

Some of the other responsibilities of data engineers are collecting, moving, and storing data. They also prepare that data for further usage in the near future. The work of data engineers is very important as they’re the ones developing the foundation that will then be further improved by data science or machine learning processes. 

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A Data Engineer’s Tool Belt 

Data engineers are well-versed in the following: 

• Python
• SQL
• Has knowledge of Java or Scala 
• Has knowledge of cloud services such as Google Cloud or Amazon Web Services 

These are the basic skills that a data engineer will surely have, but this can be further enhanced with tools and frameworks such as Tableau, Kafka, Hadoop, and Spark. 

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What Data Engineers Can Do For Your Business 

When starting or expanding your business, data engineers are typically the initial hires. They play a crucial role in designing an efficient infrastructure for storing and processing data, ensuring easy retrieval whenever necessary. Data engineers can also help out in terms of streamlining data science workflows. This will further add value to your product offerings and build out retention models as well as customer lifecycles. 

Moreover, data engineers closely collaborate with data science and business intelligence teams, building pipelines for research, reporting, and machine learning.

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Machine Learning Engineers 

Machine learning engineers are usually multifaceted. They are highly skilled programmers with knowledge of both data engineering and data science. Machine learning engineers utilize big data tools and programming frameworks to transform raw data collected from data pipelines, established by data engineers, into refined data science models. These models are typically ready to scale to fit your growing online business. 

If data engineers build and define the infrastructure, it’s the machine learning engineers who are feeding data into it. It’s the machine learning engineers who also build programs or algorithms that control bots and computers to ease your business processes. 

A Machine Learning Engineer’s Tool Belt 

Machine learning engineers combine the skills of both data scientists and data engineers. These tools include programming languages like R, C++, C, JavaScript, Scala, and Julia, as well as programming tools such as MATLAB.

They should also be experienced in distributed systems tools like consul, zookeeper, and Etcd. 

What Machine Learning Engineers Can Do For Your Business 

The role of machine learning engineers in your business will depend on the project they will be working on. Aside from developing machine learning models, they also collaborate with data engineers to create data and model pipelines. Machine learning engineers are also typically involved in the entire lifecycle of data. This includes research, design, experimentation, development, and subsequently, deployment, monitoring, and maintenance. 

Moreover, their responsibilities include researching and implementing best practices to improve the existing machine learning infrastructure. As your online business expands, the volume of data will increase. The decision to hire a data engineer or a machine learning engineer depends on your business model and current stage. Rapid growth might necessitate both roles.

Elaborating on the responsibilities of machine learning engineers, they delve into researching and implementing best practices to elevate your machine learning infrastructure. This encompasses staying updated on the latest field developments, refining algorithms, and streamlining processes for heightened efficiency and accuracy in data science models.

As your online business continues to grow, the influx of data will inevitably rise. The data surge prompts a strategic choice. One is to hire a data engineer, a machine learning engineer. Another is both, based on your business model and current stage. In rapid expansion, a holistic approach may entail leveraging both experts to address evolving demands and operational complexities.

Conclusion

In conclusion, the dynamic interplay between data engineers and machine learning engineers forms the backbone of a thriving online business in the digital age. While data engineers lay the foundational infrastructure for efficient data storage and processing, machine learning engineers bring a multifaceted approach, transforming raw data into sophisticated models ready for scalability. The careful integration of these roles is pivotal for businesses dealing with the ever-expanding volume of data. As your business evolves, the decision to hire either a data engineer, a machine learning engineer, or both, becomes a strategic imperative, ensuring that your online venture navigates the complexities of technological advancements successfully.

Embracing the expertise of these skilled professionals guarantees not only effective data management. However, they also position your business for sustained growth and innovation in the competitive online landscape. Explore more insights on Data Engineers vs. Machine Learning Engineers in our blog, and if you’re ready to enhance your team, reach out for a free consultation to find the right experts for your business needs.