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Top 5 skills for data engineers

12
min
AI, ML & Data Science

Data has gotten bigger, faster and more complex, and this has called for a new class of engineers to deal with the ever-evolving data engineering landscape. Finding people who fit into this mold should become a major priority for businesses—if it isn’t already.

Companies today are dealing with volumes of unstructured data that would have been impossible to manage just a few years ago. Powered by sophisticated data pipelines, powerful cloud storage systems and flexible analytics tools, these data management software systems are designed to collect ever-deeper insights about business’s operations, customers, competitors and more. What makes someone an in-demand data engineer in such a climate? The answer often involves technical skill. While in the past, data engineers largely served in an analytical capacity, today’s employees are more like specialized software developers, equipped with a highly refined skill set.

Under the general umbrella of data engineering, there are a variety of specific areas for top candidates to master. These are the skills that aspiring data engineers should master, and what hiring managers should look for when filling out their data engineering teams.

The 5 skills data engineers need most

While the market for data engineering skills is always changing and evolving, it’s worth taking a detailed look at the current best practices and most relevant technical abilities in this space. The following are five of the most important areas of expertise for modern data engineers to master.

1. Management of databases

One of the most reliably important skills for a data engineer is the ability to work effectively with database software. This doesn’t mean data engineers today are performing the same tasks as in the past, however—the variety of database systems associated with modern data management has increased significantly.

Today’s data engineers should be power users of relational databases, including commercial offerings from Oracle and Microsoft, open-source tools such as PostgreSQL or MySQL. They should also be proficient with non-relational databases. These include MongoDB, Apache Cassandra and a wide selection of Hadoop-based technologies. The importance of non-relational tools has risen as large, unstructured data inputs have become common parts of data management operations.

Being able to structure, load, and query the data is a foundational part of the data engineering skill set. Employees should be able to perform these actions using the technology tool that suits the organization’s needs most precisely.

2. Data processing pipelines

The database is not the only part of a functional data pipeline, which means data engineer skills must extend beyond the database itself. A data pipeline incorporates all the movements and transformations data goes through from ingestion to storage in warehouses and use in processes such as analysis.

There are specialized technology tools dedicated to building out data processing pipelines. Such tools include Apache Kafka, Storm, Flume and more. By becoming skilled in the technologies dedicated to various stages of the data processing lifecycle, data engineers can increase their overall value to organizations, taking on more varied responsibilities.

A balanced data strategy requires every step of the pipeline to be executed effectively and competently. At ingestion, it’s important to collect the right content. During warehousing, the data must be kept securely and with a suitable level of visibility. Data manipulation must be able to extract real insights from the raw materials. A great data engineer will come with the skills to master each step.

3. Programming knowledge

Perhaps the most noticeable change in recent data engineer duties has been the shift away from an analytical role and toward the software development aspect. This means top data engineers should be able to code a variety of integrations and glue a large number of market solutions together.

The breadth of a data engineer’s programming knowledge should cover coding in at least one classic software language such as Java, C#, C++ and Python, which will help them create small applications to solve their day-to-day issues around data engineering. They should also be familiar with more recent and advanced capabilities, however, to help them deliver functional tools to power their departments.

The movement of data engineers toward programming shows how far companies have come toward bespoke software development. Now, even organizations that don’t consider technology to be their primary product may be producing technology tools for internal use. A data engineer who can change along with this shift can increase in value on the job market.

4. Infrastructure management ability

There are complex storage requirements around non-relational data—its size and variety of formats makes this content tricky for companies to maintain. With this being the case, it’s no surprise that data engineers today are tasked with managing advanced infrastructure underlying their organization’s data pipelines.

This infrastructure is frequently cloud-based. Cloud computing allows companies to purchase large, flexible resources at reasonable rates, giving them the ability to store and work with large non-relational data sets. Data engineers should therefore be familiar with the major cloud providers’ data-centric offerings, including technology tools from Amazon Web Services, Microsoft Azure, Google and more.

The level of autonomy data engineers have regarding this infrastructure will likely depend on the size of the organization they work for. Whereas large, corporate organizations may have top-down mandates about the varieties of data infrastructure they should use, agile start-ups might give their data engineers the ability to make calls about which cloud services they use.

5. Visualization mastery

Once there are processes in place regarding data collection and storage, data engineers have one more task in front of them. They need to be able to draw insights from the content, creating reports that will help business employees make informed decisions. While self-service visualization tools are becoming more commonplace, data engineers still have a role to play in setting up analytics systems.

Business intelligence products such as Microsoft Power BI, Tableau, Google Data Studio and Analytics on AWS are essential tools of the trade for data engineers creating reports and visualizations. Employees who understand how to make use of multiple analytics solutions can deliver results in a wide variety of scenarios, capable of responding to specific requests from business employees.

The visualizations produced should be comprehensible by users who don’t have much coding knowledge or technical acumen. These accessible reports are designed to be a bridge between the increasingly technical data engineering team and the employees who are making business decisions. Recipients of the visualizations will include product managers and various other leaders on the strategic side of the organization.

Between those five skill areas, it’s possible to create a picture of a data engineer as a major contributor to an organization’s strategic data use. There is another side to the equation, however. The very best data engineers also possess interpersonal skills.

The extra edge: soft skills

While modern data engineers should build their technical skills to thrive within a field increasingly oriented toward software development and programming, the ability to communicate and collaborate effectively comes with its own kind of value.

Data engineers can function as a bridge between the technical and business sides of a company. In ideal cases, this doesn’t just mean handing off visualizations or setting employees up with useful reports. When data engineers can be active contributors in team meetings that include both software developers and corporate decision-makers, they deliver the type of strategic advantage that can’t be conveyed with software acumen alone.

Good communication, collaboration and compromise are defining marks of good employees even when they’re not expected to work outside of their departments. While hiring managers may initially hope to bring in “rockstar” tech employees who can solve problems single-handed, it’s usually preferable to find employees who can work in teams—data engineering today can be far more than a one-person job, so talented loners may cause more problems than they solve.

The complete package: the makings of an ideal data engineer

What makes someone uniquely suited to data engineering? At Transcenda, we know the answer to this question firsthand. We understand the ideal data engineer skill set—from the tech skills to work with cloud-based data infrastructure to high-level communication abilities—because that’s what our professionals bring to the table.

As for how we can help your organization with its data engineering needs, that depends. Based on your needs, we can offer everything from a culture reset to a direct infusion of talent and technology know-how.

Depending on where your organization is in terms of technology team maturity and development objectives, Transcenda experts can step in at any stage of an objective-driven business strategy. This could mean engineering solutions for your organization or providing a consulting experience that will help you bring in personnel with relevant data engineering expertise.

In any case, the end result is what matters. Moving forward, your organization should be prepared to deal with any data management challenges that present themselves, with a skilled data engineering team at the ready. This personnel has never been more important because dealing with massive quantities of non-relational data is a highly specialized task, but one that may separate leading companies from less successful competitors.

Contact Transcenda to learn about data engineering, consulting and more.

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