A data engineer is a rapidly expanding field that offers incredible challenges and rewards. Do you know what skills you’ll need to work as a data engineer? We’ll look at both hard and soft skills in this report.
Many businesses are searching for data engineers; if you search for “data engineer” on LinkedIn, you’ll find 88,000+ fantastic job opportunities in the United States alone. Anyone can get a job in any organization now that remote work opportunities are open to all. However, in order to be a successful candidate and be invited for an interview, you must first possess in-demand skills.
To become a data engineer, you’ll need these nine skills.
Great Advice on How to Learn Nine Skills + Advice on Getting a Job
- SQL (Structured Query)
Because data engineers move a lot of data, they use databases on a regular basis. For databases, there are two main types of database technologies: SQL and NoSQL (more on NoSQL in the next section).
Strong SQL skills allow you to build data warehouses, integrate them with other resources, and analyse the data for business purposes using databases. There are many SQL forms that data engineers may specialise in at some stage (Advanced Modeling, Big Data, etc. ), but learning the fundamentals of this technology is required to get there.
That is why all companies, from Apple to small businesses, need their data engineers to be SQL experts.
- NoSQL is a no-SQL database
This is a new form of distributed data storage that is gaining in popularity. Simply put, the term “NoSQL” refers to a technology that is not based on SQL.
Apache River, BaseX, Ignite, Hazelcast, Coherence, and many others are examples of NoSQL. You’ll almost certainly come across them during your job quest for a data engineer, so learning how to use them will come in handy.
- Python is a programming language
Python is a popular programming language that is still in high demand (it’s the third most popular among programmers). To be able to write maintainable, reusable, and complex functions, data engineers must be fluent in Python. This language is quick, scalable, and ideal for text analytics. It also provides a strong foundation for big data support.
Python is simple to learn thanks to the abundance of resources available for people of all skill levels. Please take a look at the following material for beginners:
- 7 Tools to Help You Become a Data Engineer A series of useful online courses, including a Python introduction for beginners.
- 10 Python Skills for Beginners. Beginners should master the following ten Python skills. A list of Python skills that programmers employed in data science and engineering should have.
- Exploring Python Basics. Exploring Python’s Fundamentals [Content chosen by Naomi Ceder, the Python Software Foundation’s new Chairperson]. A free eBook that covers the fundamentals of Python programming, including its features and syntax, as well as how to use Python for data modeling and accurate prediction.
- Amazon Web Services is a cloud computing provider (AWS)
- Most programmers use Amazon Web Services (AWS) to become more agile, creative, and scalable. On AWS, data engineering teams react.
If you want to learn AWS, you might look into online courses or Amazon’s own tutorials (like this one on AWS and big data). Then you can put your skills to the test and earn an official Amazon certificate – a great way to distinguish yourself as a professional.
Kafka is a real-time data processing software framework that is open-source. It means you can use it to create real-time streaming apps, which are needed by businesses. Apps that use Kafka can help discover and apply patterns, as well as respond to consumer needs in real time.
That’s why Kafka is used by 60% of Fortune 100 companies for their applications. LinkedIn, Microsoft, Netflix, Airbnb, and Target are among them. For example, the New York Times uses Kafka to store and distribute published content to apps so that readers can access it.
Data engineers use Apache Hadoop, an open-source platform, to store and analyse large quantities of data. Hadoop is a set of tools that promote data integration rather than a single framework. It’s for this reason that it’s useful for big data analytics.
If you work as a data engineer, you’ll almost certainly be using Kafka and Hadoop to process, track, and report on real-time data.
- Reading that is simple and succinct
The first soft skill on this list is writing. Many aspiring data engineers lack it, putting themselves at a disadvantage in terms of job opportunities. The following are the most significant advantages of writing for data engineers:
Consolidate your understanding. In this interview with Andrew Ng, Ian Goodfellow, an Apple data engineer, says that writing blogs helps to consolidate and solidify comprehension of complex professional concepts.
Others should be able to comprehend complex data. You may be responsible for communicating data and findings to supervisors, team members, and third parties, which necessitates the ability to write accurately and concisely.
Begin by using free tools like Grammarly to proofread your work. It will identify complicated sentences and redundant terms, as well as make suggestions for improving the coherence and clarity of your writing.
- Communication between people
A data engineer works with a range of stakeholders, including data analysts, chief technical officers, developers, designers, customers, machine learning engineers, and others.
According to LinkedIn studies, communication – including interpersonal communication – is the most needed soft skill by employers. You must master interpersonal communication skills, regardless of whether you are an introvert or lack them.
Begin with the following areas:
- Asking for and providing input to others is known as feedback (both in writing and verbally)
- Active listening is a technique for understanding other people’s experiences and being more interested in discussions.
- Learn how your stance, facial expressions, and hand movements will make people feel more at ease when speaking with you.
- Organize your time
Every aspect of a data engineer’s job can be improved if they have outstanding time management skills. In this line of work, there are a lot of things that can keep you up at night, so being able to plan your day and stick to it is a huge plus.
Time management benefits that contribute to happy data engineers include:
- Less anxiety and stress
- A more favorable work-life balance
- On-time project completion
- More time to devote to personal tasks or leisure activities
- There would be less procrastination.
The good news is that time management is something that can be learned. There are several books you can use, as well as applications like Forest and HabitMinder (which are perfect for learning preparation and sticking to schedules).