By Dorian Martin, GetGoodGrade

Do you want to get involved in data engineering?

Good idea.

A lot of companies are looking for data engineers — if you search for “data engineer” on LinkedIn, you’ll get 88,000+ great offers in the US alone. With remote work options available to everyone, you can get a job in any company. But first, you need in-demand skills to be a good candidate and get invited for an interview.

In this post:

• Nine in-demand skills to become a data engineer
• Tips to get the nine skills + career advice.

1. SQL

 
Data engineers move a lot of data around, so they use databases every day. There are two major types of database technologies for databases: SQL and NoSQL (more on NoSQL in the next section).

Strong SQL skills allow using databases to construct data warehouses, integrating them with other tools, and analyzing that data for business purposes. There are several SQL types that data engineers might focus exclusively on at some point (Advanced Modelling, Big Data, etc.), but getting there requires learning the basics of this technology.

That’s why all companies, from giants like Apple to small businesses, need their data engineers to be experts in using SQL.

2. NoSQL

 
This is a different type of distributed data storage that’s becoming increasingly popular. Simply explained, the name “NoSQL” means technology based on something different from SQL.

Examples of NoSQL include Apache River, BaseX, Ignite, Hazelcast, Coherence, and many more others. You’ll definitely get across them during your data engineer job search, so knowing how to use them would be a huge advantage.

3. Python

 
Python is the core programming language that remains in high demand (in fact, it’s the third most loved language by programmers). Data engineers are expected to be fluent in Python to be able to write maintainable, reusable, and complex functions. This language is efficient, versatile, perfect for text analytics, and gives a legit foundation for big data support.

Source
 

Learning Python is easy thanks to the availability of resources for all kinds of skill levels. Feel free to take a look at this content for beginners:

• 7 Resources to Become a Data Engineer. A collection of helpful online courses, including a beginner-friendly Python introduction
• 10 Python Skills for Beginners. A list of essential Python skills for programmers working with data science and engineering
• Exploring Python Basics. [Content handpicked by Naomi Ceder, the current Chairperson of the Python Software Foundation]. A free eBook that describes the basics of Python programming, its feature, and syntax, and using Python for data modeling and generating accurate predictions.

4. Amazon Web Services (AWS)

 
AWS is a popular cloud platform that most programmers use to become more agile, innovative, and scalable. Data engineering teams reply on AWS to design automated data flows, so you’ll need to know the design and deployment of cloud-based data infrastructure with this tool.

If you’re interested in learning AWS, you might want to try online courses or Amazon’s own tutorials (like this one on AWS and big data). Then, you can try your knowledge and get an official certificate from Amazon – a good way to stand out as a professional.

5. Kafka

 
Kafka is an open-source processing software platform for handling real-time data feeds. It means you can use it to build real-time streaming apps, which is something that businesses require. Kafka-powered apps can help discover and apply trends and react to customer needs almost in real time.

That’s why 60 percent of the Fortune 100 companies use Kafka for their applications. Among those are LinkedIn, Microsoft, Netflix, Airbnb, and Target. The New York Times, for example, uses Kafka to store and distribute published content to apps to make it available to readers.

6. Hadoop

 
Apache Hadoop is an open-source framework that data engineers use to store and analyze massive amounts of information. Hadoop isn’t a single platform but a number of tools that support data integration. That’s why it’s useful for big data analytics.

If you become a data engineer, the chance is you’ll be using Kafka together with Hadoop for real-time data processing, monitoring, and reporting.

7. Clear and concise writing

 
Writing is the first soft skill on this list. It’s something that many aspiring data engineers tend to ignore, only to deprive themselves of better career opportunities. Here are the most important benefits of writing for data engineers:

• Solidify your knowledge. Writing blogs helps to consolidate and solidify the understanding of complex professional concepts, says Ian Goodfellow, a data engineer from Apple, says In this interview with Andrew Ng
• Explain complex data to others. You might be involved in reporting data and results to managers, team members, and this-parties, which requires the ability to write clearly and concisely.

Start by checking your writing with free tools like Grammarly. It will find complex sentences, unnecessary words, and generate recommendations to make writing more coherent and clearer.

8. Interpersonal communication

 
A data engineer is someone who constantly communicates with different stakeholders, including data analysts, chief technology offers, developers, designers, clients, machine learning engineers, and others.

LinkedIn research found that communication – including interpersonal communication – was the number one soft skill wanted by employers. Whether you’re an introvert or don’t have sufficient interpersonal communication skills, you have to learn them.

Source
 

Consider starting with these areas:

• Feedback—asking for and giving feedback to others (both in writing and verbally)
• Active listening—using active listening to understand the perspectives of others and be more involved in conversations
• Body language—learn how posture, facial expressions, and hand gestures can make others more comfortable when communicating with you.

9. Time management

 
A data engineer with excellent time management skills can improve every aspect of their work. There’s a lot of things that can keep you awake at night in this career, so having the ability to plan the workday and stick to the schedule is an amazing advantage.

Benefits of time management that lead to happier data engineers:

• Less stress and anxiety
• Better work-life balance
• Project delivery on time
• More time for personal projects or recreational activities
• Less procrastination.

The good thing is that you can learn time management. There are helpful apps like Forest and HabitMinder (they’re great to help learn planning and staying true to schedules) as well as many books you can use.

 
Bio: Dorian Martin is a professional freelance writer specializing in digital marketing, SaaS, and tech. When he’s not curating content at GetGoodGrade, he loves researching writing techniques and being a student of copywriting in general. Within the past three years, Dorian graduated from multiple online universities and earned degrees in marketing.

Related:

• We Don’t Need Data Scientists, We Need Data Engineers
• 5 Supporting Skills That Can Help You Get a Data Science Job
• How Reading Papers Helps You Be a More Effective Data Scientist