What Does a Data Engineering Job Involve in 2024?
Data engineering is a hot topic in the AI industry right now. And as data’s complexity and volume grow, its importance across industries will only become more noticeable. But what exactly do data engineers do? Well, there’s a lot that goes into the job. Not only does it involve the process of collecting, storing, and processing data so that it can be used for analysis and decision-making, but these professionals are responsible for building and maintaining the infrastructure that makes this possible; and so much more.
So let’s do a quick overview of the job of data engineer, and maybe you might find a new interest.
Building and maintaining data pipelines
Data integration is the process of combining data from multiple sources into a single, consistent view. This involves extracting data from various sources, transforming it into a usable format, and loading it into data warehouses or other storage systems. Think of it as building plumbing for data to flow smoothly throughout the organization.
This is a pretty important job as once the data has been integrated, it can be used for a variety of purposes, such as:
- Reporting and analytics
- Business intelligence
- Machine learning
- Data mining
All of this provides stakeholders and even their own teams with the data they need when they need it.
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Designing and implementing data infrastructure
Data engineers are responsible for choosing and configuring the right tools and technologies to store, process, and analyze data. This might involve setting up databases, data lakes, and streaming platforms. These professionals will also work with data scientists and other stakeholders to design and implement data pipelines. Think of data engineers as the architects of the data ecosystem. They go and build the foundation and framework that allows data to be collected, stored, and analyzed.
Here are some of the specific tasks that data engineers might perform:
- Designing and implementing data warehouses and data lakes
- Configuring and managing databases
- Developing and deploying data pipelines
- Integrating data from different sources
- Ensuring the security and reliability of data
- Optimizing data performance.
Writing code and scripts
Though not thought of that much, data engineers have to be talented in writing codes and scripts. Normally, they use programming languages like Python, Java, and Scala to automate data processing tasks. They write scripts to extract data from a variety of sources, clean it, and transform it into the desired format. Just like with any other programming professional, data engineers use coding like a magic wand to manipulate and shape the data.
So being able to go in the back end isn’t unheard of and helps these professionals to clearly communicate with other members of their data teams about data needs and other issues that allow them to maintain a robust data infrastructure.
Monitoring and troubleshooting data pipelines
Data engineers keep a watchful eye on data pipelines to ensure they’re running smoothly and efficiently. They troubleshoot any issues that arise throughout the data lifecycle and move forward to fix them. Without proper monitoring and on-call troubleshooting, their ability to maintain data quality and availability can be at risk, possibly harming teams that depend on the data for important context for decision-making.
Think of it as like being a data doctor. Data engineers work to diagnose and treat any problems that might hinder the flow of information.
Collaborating with other teams
This is a big one and just like with any other data-focused profession, critical. Data engineers work closely with data scientists, analysts, and other stakeholders to understand their data needs and build solutions that meet them. This could mean of course meetings, checkups, experiments, and other ways so they communicate are able to effectively and bridge the gap between the technical aspects of data and the business needs it serves.
This means that they also have to be skilled at communicating with people who may not share their technical expertise. Although often overlooked, having a good set of soft skills allows data engineers to communicate expectations, and needs so that their teams and other teams that depend on the flow of data are well aware of the data ecosystem and they can all better work together.
It’s like being a team player, working together to unlock the insights hidden within the data.
Conclusion
Hopefully, this gave you a good bird’s eye view of what the role of a data engineer entails. These professionals are working hard to design, build, and maintain the data ecosystems that allow other professionals to make use of data in a variety of ways.
And as any data engineering professional knows, the best way to stay ahead of the curve is by keeping up with the latest in all things related to data and data engineering. The best way to do that is by joining us at ODSC’s Data Engineering Summit and ODSC East.
At the Data Engineering Summit on April 24th, co-located with ODSC East 2024, you’ll be at the forefront of all the major changes coming before it hits. So get your pass today, and keep yourself ahead of the curve.
Originally posted on OpenDataScience.com
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