New Skills and Challenges for Data Engineers

Think about the times when data engineers were the only ones who had to come up with data pipelines and maintain them?

The time, which previously seemed endless, is gradually vanishing!

The scope of the data landscape grows so rapidly that the responsibilities of data engineers increase accordingly.

The technical world always evolves and the need to learn more skills is normal, and as the world is becoming more advanced the need for developers to learn a combination of skills is the need of the time.

As data engineering and data science have always been at the intersection, both engineers must understand both fields.

Hence, what is the meaning of these new skills and responsibilities that data engineers are going to face in the future?

Let’s dive into the trenches and see how our favorite data wranglers are adapting –

1. From Pipeline Masters to Data Engineers

In the past, data engineers were only responsible for developing the data pipelines, the path that carries data from the source to its ultimate destination.

But now as the fields are becoming more and more inclusive – the need to learn additional skills has become a must!

They are gradually turning into data guides, the ones who are taking people through the complicated world of data and trying to learn and incorporate the skills of data science as well!

Hence, knowing the demands of the data scientists, analysts, and the other stakeholders is a must.

With growing skills and responsibility – it is not wrong to say that they can make or break a project as a lot depends on them!

Example- A retail company’s data engineer might not only build a pipeline that feeds customer purchase data into a data warehouse, but also create user-friendly dashboards and reports that analysts can easily access to understand buying trends.

2. Machine Learning? More Like Machine Learning Buddies

Machine learning is not a new concept, earlier people might not know about it – but now it’s a part of everyday life!

Data engineers nowadays are not working all by themselves, instead they are working as a part of the team as ML engineers and data scientists.

Collaboration is only possible when data engineers are equipped with minimal knowledge about ML fundamentals.

Don’t worry you are not supposed to be an ML Expert but understanding it at a basic level just enough to know how it works with data can produce great results.

Data engineers are now a crucial member in ML model training by gathering, preparing, and organizing the data required.

These include things like data cleansing, data engineering (generating data from data tables), and data quality control amongst others.

Data engineers benefit from understanding the inner workings of ML models, which allows them to build efficient pipelines and monitor model performance.

Example – An engineer looking at a product such as recommending items or songs to a customer of a music streaming platform, might be able to get a view on data first, run a minimum maintenance or process then feed that into an ML model which then recommends new songs.

3. The Cloud is Essential – So is to Embrace the Distributed Future

Although on-premises data storage is still there, the cloud has dethroned the thrones of big data centers.

The cloud is increasingly becoming the sought-after place for storing and processing information.

Because of this transformation, the data engineers now have to work with cloud platforms such as AWS, Azur, and GCP.

The cloud-based data streaming platform provides scalability, flexibility, and cost-effectiveness – the basic properties for dealing with large datasets.

Data engineers should learn to use cloud technologies like data storage, processing, and analytics as tools.

This involves learning and adapting cloud-based data warehousing solutions, data lakes, and big processing data frameworks.

Example- A social media company’s data engineer might shift their data infrastructure from on-premises servers to AWS cloud to manage the huge amount of usage data.

4. Communication is the Key- From Code to Conversation

The days of data engineers working in silos are over now.

Effective communication plays a centrally critical role in the success of data projects.

This implies your ability to do a good job of presenting complex technical concepts in a simplified form to non-technical audiences like business stakeholders.

Moreover, data engineers must be proficient in joint work with data scientists, analysts, and other engineers.

Now documentation is mission-critical because it ensures that data pipelines, data models, and overall data architecture are understood by all the people involved.

Example- For example, a data engineer might be required to teach a new tableau visualization tool to a marketing team. He will need to translate technical jargon to non-technical language and focus on benefits for customer behavior understanding.

So, How Are Data Engineers Dealing with It All?

The good news is that data engineers are bright and hardworking people. They are constantly learning and adjusting to an ever-changing data landscape.

The following are the strategies they’re using to fill the gap-

• Online courses and training – Several online platforms provide training that ranges from courses on cloud technologies to machine learning fundamentals down to data communication skills.
• Exploitation of Communities and Knowledge Exchange – Modern engineers depend on online communities and forums to discuss with others where they can share their experiences on the best practices.
• Internal Training – A lot of companies are providing internal training programs, so the data engineers can remain updated with recent technology trends and practice skills.

The Future is Definitely Bright for Data Engineers:

The world is a concept of constant growth; we must all upgrade not only to survive but also to thrive – however, in the field of technology, the need to learn and adapt things as quickly as possible is required.

And the same is true for data engineers – with all the additional responsibilities being added over time, it is difficult to deal with but also necessary!

The future of Data Engineering is bright and exciting – and what else will be involved as part of the skills is something that can haunt those who want to fall behind – but for those who want to become an allrounder, the future is even more exciting.

So, fellow Data Engineers, it is time for you to learn and grow into that all-rounder who knows everything about data and the science behind it!