Data engineers are the heroes who build the pipelines and systems that gather, store, and process data into actionable insights. From 2018 to 2028, the expected job growth rate for data engineers is 21%.
The demand for skilled engineers who can manage the weight of this task is rising as companies attempt to analyze and store more data than in the past. It can be difficult to know where to begin, though, if you need to learn more about data engineering or are attempting to determine if it’s the correct career path for you.
This guide will provide you with the necessary knowledge to begin your data engineering adventure. We’ll look at educational options, important skills, and useful tools to help you become a data engineering rockstar.
The Data Engineer Role
What precisely does a data engineer do, then? It is their responsibility to construct and maintain the data infrastructure, as the name implies. It is ideal to think of this role as a hybrid of a database administrator (DBA) and a software engineer.
A typical day for a data engineer could involve the following tasks:
- Using Apache Spark or Apache Flink to construct ETL pipelines
- Running queries in Postgres or Cassandra databases
- Making new MySQL table indexes
Writing code to create systems that gather, clean, and store data is part of what it means to be a data engineer. Any structured or unstructured information that you might need to manage in your business is referred to as “data” in the context of this article.
However, gathering and storing data can be a very complicated procedure. There might be a variety of data sources involved, and these sources might provide various kinds of data. The complexity of the work performed by the data engineer increases with the volume, diversity, and velocity of the available data.
Data engineers create what are known as data pipelines to guarantee that jobs are completed on time. Data loading from an on-premise database to a cloud service is one example of how data is moved into designated phases via a data pipeline.
How to Become a Data Engineer
We’ve listed the steps you need to follow to become a data engineer. The specific phases will vary depending on your present abilities and experience, but this process can help you become a data engineer from scratch.
Step 1: Consider Data Engineer Education and Qualifications
A solid background in computer science, software engineering, mathematics, or statistics is strongly advised, yet there isn’t just one recommended route. These are your choices:
- Bachelor’s Degree: A bachelor’s degree in a relevant discipline gives you a strong foundation in data structures, database administration, and computer languages.
- Self-learning: You can teach yourself the required skills if you put in the effort and have access to the correct materials. You can use online tutorials, courses, and certificates as helpful resources for your self-directed learning process.
Step 2: Build Your Data Engineer Skills
Data engineers need a diverse set of technical skills to tackle complex tasks. However, it’s challenging to create a comprehensive list of skills and knowledge for success in any data engineering role. The data science ecosystem is constantly evolving, and new technologies and systems emerge, necessitating continuous learning to stay current.
Learn about database management
Most data engineer’s workdays are dedicated to managing databases, whether for data collection, archiving, transfer, cleaning, or simple consultation. Data engineers therefore need to be well-versed in database administration.
This means being proficient in several of the most widely used SQL dialects, such as MySQL, SQL Server, and PostgreSQL, as well as being fluent in SQL (Structured Query Language), the fundamental language for interacting with databases.
Data engineers must be conversant with NoSQL (“not only SQL”) databases in addition to relational databases, as these systems are quickly taking the lead for big data and real-time applications.
Learn a few programming languages
Coding is an essential ability for data engineers, just like it is for other data science positions. Data engineers utilize a variety of programming languages, in addition to SQL for a variety of jobs.
While there are other computer languages available for use in data engineering, Python is unquestionably the finest choice.
In data science, Python is the language of choice since it’s ideal for constructing data pipelines and carrying out ETL tasks.
Study up on the frameworks for distributed computing
Distributed systems have become commonplace in data science in recent years. A distributed system is a computing environment where different components are dispersed over a number of machines on a network, collectively referred to as a cluster.
By distributing the work among the cluster, distributed systems coordinate efforts to finish the task more quickly.
Massive data processing is the focus of distributed computing frameworks like Apache Hadoop and Spark, which lay the groundwork for some of the most amazing big data applications. A certain level of proficiency with one of these frameworks is a prerequisite for any prospective data engineer.
Expand your understanding of cloud computing
One of the hottest subjects in data science is cloud computing. The market for cloud-based solutions is evolving quickly. These days, a big part of what it means to be a data engineer is integrating cloud-based tools with the business systems of your organization.
Thanks to the growth of services like Google Cloud, Azure, and Amazon Web Services (AWS), the entire data workflow can now happen in the cloud. Because of this, a competent data engineer needs to be knowledgeable about cloud services, including how to use them, their benefits, drawbacks, and how to apply them to big data projects.
Given that these are the most popular platforms, you should at the very least be conversant with AWS or Azure.
Acquire a working understanding of ETL frameworks
Building data pipelines using orchestration frameworks and ETL technologies is one of the primary responsibilities of data engineers.
We could name a lot of technologies in this section, but the data engineer should be familiar with or at ease with some of the most well-known ones, such Apache Airflow and Apache NiFi.
An orchestration framework is called Airflow- It is an open-source tool for data pipeline design, generation, and tracking. For a simple, repeatable big data ETL procedure, NiFi is ideal.
Study frameworks for stream processing
Real-time data is used in some of the most inventive data science applications. Consequently, there is an increasing need for applicants who are conversant with stream processing frameworks. For data engineers who want to advance in their careers, understanding how to use streaming processing platforms like Flink, Kafka Streams, or Spark Streaming is a smart choice.
Study Shell
Shell commands and scripts are used to carry out the majority of tasks and operations performed by the Cloud and other Big Data tools and frameworks. To edit files, execute commands, and navigate the system, data engineers need to be at ease using the terminal.
Step 3: Get the Essential Tools
Once you’ve perfected your skill, you’ll want the appropriate tools to complete your tasks. Here are a few popular options:
- Jupyter Notebook is a web-based interactive platform for data analysis and visualization.
- Apache Spark is a sophisticated open-source platform for massive data processing.
- Apache Hadoop is a platform for the distributed storage and processing of massive datasets.
- ETL/ELT Tools: Data pipelines can be automated using tools such as Apache Airflow and Luigi.
- Cloud-Based Platforms: Cloud platforms such as AWS Glue and Azure Data Factory provide data integration and management capabilities.
Step 4: Work on your data engineer portfolio
To advance in your career as a data engineer, you will need to work on projects that showcase your abilities and fundamental knowledge.
To impress prospective employers, you’ll want to showcase the abilities we’ve already discussed, which call for working on a range of diverse tasks. You may work on your own projects with DataCamp Workspace’s collaborative cloud-based notebook, which enables you to analyze data, communicate with others, and share ideas.
Additionally, you can use your skills to contribute to a variety of data science projects, which will help you build your data engineering portfolio and address real-world issues right from your browser.
Step 5: Apply For Your First Job as a Data Engineer
In the data science sector, one of the most sought-after jobs is data engineering. Businesses are looking to hire data engineers to help them scale and make the most of their data resources, from large tech in Silicon Valley to small data-driven startups across sectors.
Step 6: Prepare for the Data Engineering Interview
Interviews for data engineering typically have two sections: one for technical questions and another for non-technical ones. But you should anticipate questions pertaining to four subjects:
Your experience and CV
Your experiences relevant to the data engineering role will be of interest to recruiters. Make sure your CV highlights your prior experience working on data science positions and projects, and be ready to go into great detail about them.
Recruiters will need this information to evaluate your problem-solving, communication, and project management skills, in addition to your technical abilities.
Coding
Most likely, this is the most difficult aspect of the data science interview. Typically, you will be required to use Python or a data framework such as Spark to solve an issue in a few lines of code in a short amount of time.
One thing you may do is create a basic data pipeline that loads and cleans data. Even if there shouldn’t be much complexity to the problem, the pressure of the moment can hinder your effectiveness.
If this type of test is unfamiliar to you, you can consider practicing beforehand with some coding problems.
SQL
Without strong SQL knowledge, you won’t advance in your data engineering profession. This is the reason you can be required to tackle a SQL-based task in addition to the programming test. Writing effective database queries to perform certain data processing is usually the exercise’s focus.
System design
This is another hard and most conceptual portion of the technical interview. One of the most important jobs performed by data engineers is designing data architectures.
You will be required to provide a comprehensive data solution in this section, which typically consists of three components: data modeling, data processing, and data storage.
There are countless design possibilities due to the data science ecosystem’s continually expanding breadth. You must be prepared to go over the benefits and drawbacks, as well as any potential compromises with your decisions.
Conclusion
As one of the most in-demand positions in the data science industry, data engineering is undoubtedly a fantastic career option for aspirational data professionals.
By following the steps outlined in this guide, you’ll gain the knowledge, skills, and tools you need to navigate the ever-growing sea of data.
