Data engineers are in a highly competitive space. This guide explains the role of different soft skills in data engineering and how to develop them to land a successful data engineering role.
Here’s what we’ll cover:
For a successful and rewarding career as a data scientist, you need to master the related technical skills. From mathematics and statistics to analytics and modeling to programming and data visualization, these hard skills will help you with the fundamentals of the job.
However, in the data engineering field, soft skills play an increasingly important role.
In fact, more than 4,000 software engineers, developers, managers, and executives surveyed in LinkedIn’s Workplace Learning Report chose soft skills as their number one priority for talent development for data engineers and those in related fields.
Even with mastery of the technical skills, failing to acquire the soft skills needed can inhibit your effectiveness.
You may get asked about your data engineering soft skills during your job search. You’ll need these skills once you are on the job. Here are the top 10 data engineering soft skills you need.
Most data engineers place top value on their technical skills and can easily underestimate the importance of clear, effective, and professional communication.
Data engineers build and use complex systems to collect, manage, and convert raw data. They transform raw and unstructured data into structured data and formats for analysis by data scientists and business leaders. They manage big data, varying data sets, data warehousing, and data prep, meaning they take complex and disparate data and make it easy to understand. The same applies to communicating information.
Data engineers will likely interact with many different stakeholders — many with different levels of technical expertise. A good data engineer will need strong listening and effective communication skills to ensure they can explain complex information in a way every stakeholder can understand.
Just as important as communication skills, data engineers need to be able to work with teams. You need to be able to work with people from many different backgrounds and personality types. There may be people you don’t get along with or see eye-to-eye with about things. Still, the ability to collaborate on projects is often necessary to achieve optimal results.
Data and data infrastructure is finite and definable. Data engineers, however, must be flexible and adaptable. As projects change or evolve, they must be able to reprioritize and adjust. When things don’t go as scheduled, engineers must be able to find a different solution. Failing to do so can lead to frustration, missed deadlines, and wasted resources.
Failing to adapt to changes as they occur may also impact how supervisors and managers view your ability to handle significant challenges.
As a data scientist, leadership can apply to managing projects and challenges as well as leading people. As you progress in your career, you will likely work on more complex data science problems and be in charge of groups. You need to develop the skills to create trust with others to accomplish your team’s goals.
Emotional intelligence (EQ) is being aware of, and in control of, your emotions and being aware of the emotions of others. EQ helps create better relationships within the workplace and promotes a positive environment.
EQ includes the ability to self-regulate your behavior. For example, even if you are frustrated or upset about a task you’ve been given, you need to regulate your emotions and complete the task efficiently to maintain a professional appearance.
You’ll also need empathy to identify and understand how others feel. For example, if a team member is showing signs of being overwhelmed by their workload, you need to be aware of what’s happening and take steps to help alleviate the situation.
You are not always going to agree with everyone you work with. The ability to resolve disagreements without causing additional stress is an essential trait for data engineers.
When you find yourself in conflict with someone else, it helps to shift perspective. Instead of judging them, for example, use your analytical skills to try to understand their position.
Your data analysis is likely helping others to make important business decisions. It’s important to understand how your work fits into the bigger picture within an organization.
This perspective can help you gain greater insights into the impact of your work. Data analysts should not only understand the big data in their SQL database, data warehouse, or data lake, but they also need to have a firm grasp of the context in which the data will be used.
Managers want to work with people they can trust to get the job done well without needing constant micromanagement. You will be expected to meet deadlines, ensure that your work is free of errors, and solve problems on your own.
If you showcase a disciplined approach to doing your job and a strong work ethic, you will likely be rewarded with more flexibility and independence in how you achieve your goals due to your reliability.
Data mining projects may require a creative approach to problem-solving. Being able to apply logic and think differently to overcome obstacles is one facet of creativity. Great data scientists are also able to dig beneath the surface and look for trends and patterns in data.
It takes a creative approach to work in an area without a blueprint and to apply original thinking. For example, when a query into a data warehouse doesn't reveal the results you need, you need to employ a creative approach to uncover the big data you need.
Perhaps no data engineering soft skill is more important than applying critical thinking. You need to be able to see past the obvious, tune out distractions, and set aside preconceived notions to look at a data model critically to make informed decisions.
Your job may require you to have a deep understanding of programming languages and analytics platforms. You may need to know the Python programming language, Java, and Apache Hadoop. You will need expertise in architecture and infrastructure, including different cloud computing platforms such as AWS, Microsoft Azure, or Google Cloud.
Even with a mastery of these skills and/or a bachelor's degree in computer science, it doesn't mean you've mastered the soft skills you need to succeed.
Fortunately, there are ways to enhance your soft skills to prepare for a career in data science or to enhance your skills. Data professionals can learn a lot by observing others and how they interact with those around them. Whether you are a new grad, an early-to-mid career professional looking to land a better job, or looking to upskill, Springboard can help you learn the skills you need to improve and grow.
Ready to switch careers to data engineering?
Data engineering is currently one of tech’s fastest-growing sectors. Data engineers enjoy high job satisfaction, varied creative challenges, and a chance to work with ever-evolving technologies. Springboard now offers a comprehensive data engineering bootcamp.
You’ll work with a one-on-one mentor to learn key aspects of data engineering, including designing, building, and maintaining scalable data pipelines, working with the ETL framework, and learning key data engineering tools like MapReduce, Apache Hadoop, and Spark. You’ll also complete two capstone projects focused on real-world data engineering problems that you can showcase in job interviews.
Check out Springboard's Data Engineering Career Track to see if you qualify.
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