Data Engineering, characterized by its rapid expansion, offers a diverse array of career avenues. With giants like Google, Facebook, Quora, Twitter, and Zomato generating vast volumes of data at an unparalleled rate, the demand for skilled professionals is soaring. Organizations are swiftly adopting Big Data Technology to harness and leverage this influx of data. Are you eager to ride this wave of opportunity? To thrive in this dynamic field, it’s essential to cultivate key data engineer skills, mastering the essential techniques necessary for success. In this article, you will learn about the data engineer skills required for you, also what skills required for the data engineer job description to get.
Data engineers play a crucial role in data science projects, focusing on the development and maintenance of data architecture. They ensure seamless data flow between servers and applications, bridging the worlds of software engineering and data science. Their key responsibilities encompass creating data collection methods, integrating new technologies, and optimizing foundational data processes.
Data Engineers are responsible for storing, pre-processing, and making this data usable for other members of the organization. They create the data pipelines that collect the data from multiple resources, transform it, and store it in a more usable form.
Mastering key data engineer skills and essential data engineering skills is crucial for success in the rapidly evolving field of data engineering. These skills empower professionals to navigate complex data landscapes and drive innovation in today’s data-driven world.
Entering 2023, data engineering emerges as a pivotal and highly rewarding career choice. Recent projections indicate that the global data engineering and big data services sector is set to exceed $77.37 billion by year-end, with a remarkable 17.60% compound annual growth rate.
Furthermore, a LinkedIn survey underscores the industry’s momentum, revealing a 40% surge in job openings for data engineering roles, significantly outpacing the 10% increase in data science positions. Remuneration is equally compelling, with Glassdoor data from May 2022 indicating that the average data engineer earns an annual salary ranging from $115,176 to $168,000. These figures paint a bright future for the field.
Let’s now explore top 9 data engineer skills that you must have to succeed in this field!
Programming provides us a way to communicate with machines. Do you need to become the best in programming? Not at all. But you will definitely need to be comfortable with it. You will be required to code the ETL process and build data pipelines.
The following programming languages are the most popular among data engineers:
It is one of the easiest to learn a programming language and has the richest library. I have found Python to be a lot easier to perform machine learning tasks, web scraping, pre-process big data using spark, and also is the default language of airflow.
If you want to learn Python, here is a great free course you can refer to our course on Introduction to Python !
When it comes to data engineering, the spark is one of the most widely used tools and it is written as Scala. Scala is an extension of the Java language. If you are working on a Spark project and want to get the maximum out of the spark framework, Scala is the language you should learn. Some of the spark APIs like GraphX is only available in the Scala language.
Here are some of the recommended resources to get started with:
You can’t get away from learning about databases when you are aspiring to become a data engineer. In fact, we need to become quite familiar with how to handle databases, how to quickly execute queries, etc. as professionals. There’s just no way around it!
SQL databases are relational databases that store data in multiple related tables SQL is a must-have skill for every data professional. Whether you are a data engineer, a Business Intelligence Professional, or a data scientist – you will need Structured Query Language (SQL) in your day to day work.
You should know
Want to get answers to all these questions? Here are some of the best resources to clear your doubts:
The sheer fact that more than 8,500 Tweets and 900 photos on Instagram are uploaded in just one second blows my mind. we are generating data at an unprecedented pace and scale right now in the form of text, images, logs, videos, etc.
To handle this much amount of data, we need a more advanced database system that can run multiple nodes and can store as well as query a huge amount of data. Now, there are multiple types of NoSQL databases, some of them are highly available and some of them are highly consistent. Some are column-based, some are document-based and some are graph-based.
As a data engineer, you should know, how to select the appropriate database for your use-case and how to write optimized queries for these databases. Here are some of the resources that will help you get started with the NoSQL databases.
Automation of work plays a key role in any industry and it is one of the quickest ways to reach functional efficiency. Apache Airflow is a must-have tool to automate some tasks so that we do not end in the loop of manually doing the same things again and again.
Mostly data engineers have to deal with different workflows like collecting data from multiple databases, pre-process it, and upload it. Consequently, it would be great if our daily tasks just automatically trigger on defined time, and all the processes get executed in order. Apache Airflow is one such tool that can be very helpful for you. Whether you are Data Scientist, Data Engineer, or Software Engineer you will definitely find this tool useful.
You can deep dive into some of these concepts with these clear articles and their examples –
It is the most effective data processing framework in enterprises today. It’s true that the cost of Spark is high as it requires a lot of RAM for in-memory computation but is still a hot favorite among Data Scientists and Big Data Engineers.
Organizations that typically relied on Map Reduce-like frameworks are now shifting to the Apache Spark framework. Spark not only performs in-memory computing but it’s 100 times faster than Map Reduce frameworks like Hadoop.
It provides support in multiple languages like R, Python, Java & Scala. It also provides a framework to process structures data, streaming data, graph data. You can also train machine learning models on big data and create ML pipelines.
If you want to learn spark, here are some resources you can refer to –
It is an amazing collection of three open-source products — Elasticsearch, Logstash, and Kibana.
More than 3000 companies are using ELK stack in their tech stack, including Slack, Udemy, Medium, and Stackoverflow. Here are some of the free resources from where you can start learning ELK Stack.
Hadoop is a complete eco-system of open source projects that provide us the framework to deal with big data.
We know that we are generating data at a ferocious pace and in all kinds of formats is what we call today as Big data. But it is not feasible storing this data on the traditional systems that we have been using for over 40 years. To handle this massive data we need a much more complex framework consisting of not just one, but multiple components handling different operations.
We refer to this framework as Hadoop and together with all its components, we call it the Hadoop Ecosystem. Here are some of the free resources to know more about Hadoop.
Tracking, analyzing, and processing real-time data has become a necessity for many businesses these days. Needless to say, handling streaming data sets is becoming one of the most crucial and sought skills for Data Engineers and Scientists.
We know that some insights are more valuable just after an event happened and they tend to lose their value with time. Think of any sporting event for example – we want to see instant analysis, instant statistical insights, to truly enjoy the game at that moment, right?
For example, let’s say you’re watching a thrilling tennis match between Roger Federer v Novak Djokovic.
The game is tied at 2 sets all and you want to understand the percentages of serves Federer has returned on his backhand as compared to his career average. Would it make sense to see that a few days later or at that moment before the deciding set begins?
Kafka is a much-needed skill in the industry and will help you land your next Key Data Engineer Skills role if you can master it. Here are some of the resources that you can refer to:
AWS is Amazon’s cloud computing platform. It has the largest market share of any cloud platform. Redshift is a data warehouse system, a relational database designed for query and analysis. You can query petabytes of structured and semi-structured data easily with Redshift.
Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. Most of the data engineering job descriptions specifically list Redshift as a requirement.
Here are some of the resources to get started with the Amazon Redshift.
In conclusion, the world of data engineering thrives on essential skills like database management, ETL processes, and programming languages. As industries evolve in the data-driven landscape, mastering these key data engineer skills becomes paramount for success. Whether entering the field or enhancing expertise, investing in these abilities unlocks doors to endless opportunities in the data-driven era.
Hope you like the article and getting understanding about the data engineer skills and what skills required for the data engineer job description.
Do you have any other skills that you wish were on this list to become a data engineer? Let me know in the comments!
A. Data engineers need skills in database management, ETL processes, data modeling, data warehousing, and programming languages, along with a grasp of relevant tools and technologies.
A. Data engineers should possess hard skills in SQL, NoSQL, Hadoop, Spark, data pipeline development, data integration, scripting languages (e.g., Python), and familiarity with cloud platforms (e.g., AWS, Azure, GCP).
A. Yes, data engineers require coding skills. Proficiency in languages like Python, Java, or Scala is essential for tasks like building data pipelines and automating processes in data engineering.
A. The typical role of a data engineer involves designing and maintaining data architectures, creating ETL pipelines, ensuring data quality, collaborating with data scientists and analysts, and optimizing data storage for efficient analytics and reporting.
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