9 Must-Have Skills to Become a Data Engineer!

Lakshay Arora 09 Oct, 2023 • 9 min read

Introduction

Data Engineering is one of the fastest-growing fields with a heterogeneity of job opportunities. From Google, Facebook, Quora, Twitter, Zomato everybody is generating data at an unprecedented pace and scale right now. Organizations are swiftly embracing Big Data Technology to manage and utilize this data effectively. Interested in joining this wave? To excel in this career, it’s vital to grasp the fundamental data engineering skills required.

What is a Data Engineer?

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.

Career Growth in Data Engineering

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.

Top 9 Skills to Become a Data Engineer

Let’s now explore top 9 data engineer skills that you must have to succeed in this field!

Programming Languages

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.

Data Science Skills - Programming

The following programming languages are the most popular among data engineers:

Python

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 !

Scala

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:

SQL Databases

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!

Data Engineer Skills - SQL Databases

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

  • How to insert, update, and delete records from your database?
  • How to create reports and perform basic analysis using SQL’s aggregate functions?
  • How to perform efficient joins to fetch your data from multiple tables?

Want to get answers to all these questions? Here are some of the best resources to clear your doubts:

NoSQL Databases

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.

Data engineer skills - NoSQL Databases

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.

Apache Airflow

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.

Apache Airflow

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 –

Apache Spark

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.

Apache Spark

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 –

ELK Stack

It is an amazing collection of three open-source products — Elasticsearch, Logstash, and Kibana.

ELK Stack
  • ElasticSearch: It is another kind of NoSQL database. It allows you to store, search, and analyze a big volume of data. If the full-text search is a part of your use case, ElasticSearch will be the best fit for your tech stack. It even allows search with fuzzy matching.
  • Logstash: It is a data collection pipeline tool. It can collect data from almost every resource and makes it available for further use.
  • Kibana: It is a data visualization tool that can be used to visualize the elasticsearch documents in a variety of charts, tables, and maps.

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 Ecosystem

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.

Hadoop Ecosystem

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.

Apache Kafka

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.

Apache Kafka

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 data engineer role if you can master it. Here are some of the resources that you can refer to:

Amazon Redshift

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.

Amazon 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.

Conclusion

In conclusion, the world of data engineering is witnessing unprecedented growth and demand, fueled by the data-driven landscape of today’s organizations. To thrive in this dynamic field, mastering essential skills is paramount. From database management and ETL processes to programming languages and data modeling, a strong foundation is key. As we navigate the data-rich future, those equipped with these skills will continue to play a pivotal role in shaping industries, driving innovation, and unlocking the true potential of data. So, whether you’re embarking on a data engineering career or looking to enhance your existing expertise, investing in these skills is a forward-thinking decision that opens doors to a world of opportunities in the data-driven era.

Do you have any other skills that you wish were on this list to become a data engineer? Let me know in the comments!

Frequently Asked Questions

Q1. What skills are required for a data engineer?

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.

Q2. What are hard skills for data engineers?

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).

Q3. Do data engineers need coding skills?

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.

Q4. What is the typical role of a data engineer?

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.

Lakshay Arora 09 Oct 2023

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