Soft skills can be almost as important as data engineering skills when you apply for a job. Soft skills can make the difference between stress and efficiency or being unsatisfied with your position and a raise.

When data engineers and data scientists earn bachelor’s degrees, they usually take classes in topics like data warehousing, programming languages, machine learning, and data science. Unfortunately, most colleges and universities don’t require courses that teach the “soft skills” that can help engineers succeed.

On-the-job experience is a chance to add additional technical skills, like working with Amazon AWS or Google Cloud in a live environment. But it’s also a chance to practice and develop the essential soft skills that allow you to be a valued part of a data engineering team.

When data engineers want to move their careers forward, they often need to learn the following six skills:

Table of Contents

  1. Public Speaking
  2. Expository Writing
  3. Interpersonal Communication
  4. Adaptability 
  5. Stress Management 
  6. Time Management

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1. Public Speaking

Data science allows you to hide behind reports and data visualizations, but sometimes you have to deliver those reports in person. Experts believe that up to 75% of people may feel frightened of public speaking. For some, public speaking causes mild nervousness. Others feel so afraid that they break out in hives, which prevents them from walking in front of an audience.

Without strong public speaking skills, data engineers cannot communicate their findings to managers and colleagues.

Some ways to improve public speaking skills include:

  • Spending time writing and practicing presentations
  • Dressing professionally to feel more confident
  • Smiling before and during the presentation
  • Practicing in front of a mirror to gain experience
  • Including graphics in the presentation to shift the focus from the speaker to the message

You can also enroll in a public speaking course at a nearby college or university. Most Communications Departments will let professionals take a few tutorials to improve their skill sets.

If you feel so much anxiety that you cannot tolerate even thinking about speaking to a group, then you may want to talk to your doctor about finding a medication or counselor. Cognitive behavior therapy (CBT) works well for most people who experience anxiety in specific situations.

2. Expository Writing 

Expository writing aims to explain or "expose" information. Data engineers, as well as data scientists and data architects, all need to be able to expose the important data and results to upper management. But data science professionals live in a world of numbers and analytics.

Managers and other business leaders don’t always have the computer science background needed to understand things like data warehouses and Big Data analytics. They may understand the importance of using data analytics in business intelligence, but that doesn’t mean they know how to read raw reports.

Strong writing skills require several sub-skills like:

  • Structuring sentences for clarity
  • Organizing paragraphs so they follow a coherent thought process
  • Summarizing complicated findings into a concise brief that readers can scan quickly
  • Knowing when to include graphics to make results clearer
  • Avoiding engineering jargon and excess material that waters down your main points (for instance, say "artificial intelligence" instead of machine learning or NLP)
  • Working in new written forms, like scripting explainer videos and annotating data visualizations

The mechanics of good writing also matters. Reports with poor subject-verb agreement and other basic inaccuracies can make useful reports look unprofessional and irrelevant. Consider using an app like Grammarly, Hemingway Editor, and Readability Score to avoid common mistakes.

3. Interpersonal Communication 

Data engineers often work with machine learning engineers, data analysts, CTOs, developers, and other experts. Therefore, interpersonal communication is an essential soft skill that can help data engineers lead teams and execute projects.

Some people struggle with interpersonal communication because they consider themselves introverts. Regardless of your personality type, you won't succeed as a data engineer unless you have strong communication skills.

Build your interpersonal communication skills by:

  • Following the active listening algorithm to understand other perspectives
  • Paying attention to your body language to make sure you are receptive to other people
  • Focusing on how you present yourself to others
  • Asking managers for constructive feedback

Individuals accomplish more when they work with their colleagues. Remember that your job contributes to a larger goal - when you're asked to contribute to a data mining project, it's often because there's a specific business need. As your communication skills improve, you assume more leadership qualities that make you stand out from other data engineers.

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4. Adaptability 

With every project, there's a high chance that something will go wrong or change suddenly. Adaptability is absolutely necessary for data engineers. Learning to adapt will help you change strategies mid-project without getting too frustrated. If you don’t respond well to small changes, then your manager may worry about how you handle large-scale changes.

Many professionals find that they can become more adaptable by:

  • Remembering that change isn’t personal and is inevitable
  • Stepping back to consider why a manager may have altered the project
  • Breaking projects into multiple stages instead of thinking of them as a single job

Now, there are times where you shouldn't adapt. If you see a legitimate problem or negative outcome with changing strategies, then be sure to voice your concerns. Data engineers are an important voice in strategic decisions.

5. Stress Management

According to a 2018 Gallup poll, 23% of employees say that they feel “burned out” always or very often. Nearly 45% say that they feel burned out sometimes.

Data engineers are more likely to feel burnout when they have too much work to finish, when their role is too unstructured, or when they don’t feel supported by their managers. However, there are ways to prevent burnout like stress management. The American Psychological Association recommends managing stress by:

  • Learning healthy ways to relax, such as taking a walk, taking deep breaths, or practicing mindfulness
  • Tracking stressors by recording thoughts and feelings in a journal
  • Establishing borders that help maintain a work-life balance
  • Taking time out to recharge before stress becomes a serious problem
  • Talking to supervisors about setting expectations and defining a position’s role
  • Planning your work ahead of time and avoiding unstructured days
  • Have conversations with colleagues about something other than data analysis and data modeling

Managing stress and tracking your stressors take practice. Commit to making changes at work and in your life so you can prevent burnout. A data engineer's work is never done, so make some time for self-care. Don't rely on another cup of java to get you through the day.

6. Time Management 

Time management skills can have a large-scale effect on every aspect of a data engineer's work - increasing productivity, decreasing stress, and giving you more opportunities to explore your interests.

Luckily, time management is one of the easiest skills for most data engineers to learn. It’s all a matter of practice. Some time management options that many people find useful include:

  • Prioritizing essential tasks so you don’t feel rushed at the end of the day
  • Setting deadlines that ensure you reach milestones on time.
  • Focusing on one task at a time instead of multitasking.
  • Taking regular breaks to refresh your mind and body.
  • Learning to delegate work to other people on your team.

You can also access plenty of applications that can help data engineers to manage their time more effectively. Top apps include:

  • Timely, which lets you compare your expectations with how long tasks actually take
  • RescueTime, which provides real-time tracking to show how you spend your time online so can spot websites that you might want to block while you work
  • Workflow, which lets you execute a series of tasks with one tap
  • Marinara Timer, which uses the Pomodoro Method to help you concentrate and plan breaks

Some of these applications are Microsoft Windows or Mac OS only, although RescueTime does offer Linux support. Check out the tutorials on these apps for tips on improving your time management.

Final Thoughts

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Soft skills don’t always seem important to data engineers when they’re developing their careers. To be fair, employers don’t always ask for them. An advert for a big data engineering job will typically focus on technical skills like Python and SQL, or NoSQL and Hadoop, rather than delegation and writing.

But soft skills can make life a data engineer's life infinitely easier. As a data engineer, you’ll work with a team of other computer science professionals, and you’ll need to be able to collaborate with them. And, away from the world of Apache Spark and data warehousing, there are end-users and decision-makers who need to work with you. Soft skills will help you succeed with anyone, on any project.

Xplenty is an ETL platform for creating automated data pipelines with little to no code, which saves time even if you have a software engineering background. You can integrate on the fly and add your own SQL queries as needed. Xplenty works with relational databases and NoSQL, and it supports data warehouses as well as unstructured repositories such as Hadoop. Contact us to schedule a demo and risk-free trial.