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So you want to become a data engineer? That’s great!
The data engineer career is one of the most in-demand and highest-paying jobs in the tech industry, with an annual average salary of $117,295.
Breaking into tech can seem daunting, but with the proper guidance, you can make it happen!
But how do you become a data engineer?
In this blog post, we will walk you through the steps on how to become a data engineer and make your dream a reality. We’ll cover everything from learning data engineering skills to finding a job in the industry.
Let’s get started!
How To Become a Data Engineer
Becoming a data engineer is a multi-step process.
Here are the seven steps you need to take:
Step 1: Learn the Basics of Data Engineering
If you’re new to data engineering, the first step is to learn the basics. This means understanding how data engineering fits into the larger field of data science and becoming familiar with common tools and technologies.
What is Data Engineering?
Data engineering is the process of designing, constructing, integrating, and maintaining data systems. It is a multi-disciplinary field that uses scientific principles to design and build data-intensive systems
Simply put, data engineering connects all the data in a business together and ensures that they are usable by analysts or executives!
To learn more about the details of data engineering, I’d rather you check out some of these awesome resources you can use to learn more about the basics of data engineering.
What Are Some Common Data Engineer Tasks?
Now that you know what data engineering is, let’s take a look at some of the common tasks a data engineer is responsible for:
- Designing and building data warehouses
- Creating ETL (extract, transform, load) processes to move data from one system to another
- Integrating data from multiple sources
- Cleaning and preparing data for analysis
- Optimizing data pipelines for performance
- Monitoring data systems for issues
- Securing data against unauthorized access
These are just a few of the many tasks that a data engineer is responsible for!
To learn more about the day-to-day life of a data engineer, I recommend checking out this great YouTube video from Seattle Data Guy.
I’d recommend looking at the following places for an idea of what data engineering is:
- YouTube Videos
With a combination of these learning resources above, you’ll gain a better idea and insight into the world of data engineering.
Step 2: Develop Your Data Engineering Skills
The next step to becoming a data engineer is to develop the necessary skills.
Data engineering requires a specific skill set that can be acquired through education and/or experience.
There is no one-size-fits-all when it comes to education and becoming a data engineer.
The most important thing is that you have the skills and knowledge necessary to excel in the role.
Data engineers typically require a few key skills to succeed.
- Strong analytical and problem-solving skills
- Familiarity with SQL and NoSQL databases
- Proficiency in at least one programming language (Python, Java, etc.)
- Experience with big data processing tools (Hadoop, Spark, etc.)
- Ability to effectively communicate complex technical concepts
Developing these skills will give you a strong foundation to start your data engineering career!
Based on my own personal experience as a data analyst, data engineers work mostly on creating a data warehouse and data storage.
Let’s look at which skills are found in job postings.
A data engineering role would typically include these skills:
- Data modeling (to handle raw data)
- Data warehousing
- Machine learning
- Data architecture
- Data structures
- Data analysis
- Big data
- Database management
- Statistical analysis
- Data pipeline creation
- Data analytics (more on data analytics here)
Many of these skills overlap with similar careers in data such as data scientists, data analysts, and software engineers.
Here are some common tools used by most data engineers:
- SQL: MySQL, PostgreSQL, SQL Server, NoSQL
- Databases: MongoDB, Cassandra, HBase
- Big Data Processing Tools: Hadoop, Spark
- Programming Languages: Python, Java, Scala
- ETL Tools: Talend, Informatica
- Data Visualization Tools: Tableau, Qlikview
If you’re starting from scratch, you can either learn these skills through a degree in computer science or learn through online platforms.
Taking a degree in computer science might not be for you, so if you’re thinking of self-learning, check out my recommendation below.
I’d recommend including a combination of the following:
- Online Courses
- YouTube Tutorials
- Boot Camps
- Coding Practices
- Online Data Engineer Blogs
By learning from a combination of all these resources, you’ll get a better grasp of essential data engineer skills.
These skills are not limited to a data engineer and can overlap with a data scientist role or a even database administrator role.
Step 3: Find Areas to Apply Your New Skills
Now that you’ve grasped a strong foundation of basic skills, you’re ready for the next step.
Your next step in your data engineer roadmap should begin with looking for areas to apply your newfound skills! Having some practical experience makes employers notice you better too.
I’d recommend looking for projects and competitions to sharpen your data engineer skills!
Here are a few ways you can find data engineering projects:
- Try your hand on a Kaggle Dataset
- Explore a project based on your personal interests/hobbies
- Volunteer for a side project at your workplace.
These projects will help you work on your technical skills such as data warehousing, processing data, building data pipelines, and cloud computing.
If possible, try going for real-world projects that include the entire data engineering pipeline, from data collection to data warehousing, to writing scripts to create data processing systems, creating database design, using operating systems, and cloud storage.
As for competitions, I’d recommend looking at Kaggle for simple competitions.
Step 4: Present Your Skills On A Platform
The next step in how to become a data engineer is to present your skills on a platform.
One of the best ways to show off your skills and get noticed by employers is by having an online presence.
I recommend building a personal website or blog to showcase your work, skills, and experiences as a data engineer.
This is a great way to show potential employers that you’re serious about the career and have the skills to back it up.
Not only will this help you get noticed, but it’ll also help you network with other data engineers.
If you don’t know how to build a website, there are plenty of resources online that can help you get started.
Once you have a website up and running, make sure to populate it with content that’ll showcase your skills as a data engineer.
Some examples of content you can include are:
- Projects you’ve worked on
- Tutorials or blog posts you’ve written
- Skills you have
- Presentations or talks you’ve given
Another method I use to build my online presence is LinkedIn!
By connecting with like-minded aspiring data engineers, you’ll find that the learning journey gets easier.
You might even want to connect with other positions in the field such as a data scientist or a data architect.
Having an online presence is a great way to show potential employers that you’re serious about the career and have the skills to back it up.
Step 5: Practice Interview Questions
The next step in how to become a data engineer is to start practicing interview questions.
Interviewing can be a daunting task, but it’s important to remember that employers are not looking for perfect answers.
They’re looking for how you think and how you problem-solve.
With that in mind, here are a few general tips to help you prepare for your data engineering interview:
- Review the job description and make sure you’re familiar with the skills they’re looking for.
- Practice questions with a friend or family member.
- Take some time to relax before the interview.
- Be yourself! Employers are looking to see how you think and how you problem-solve.
By following these tips, you’ll be well on your way to nailing your data engineer interview.
Data engineering is a rapidly changing field, and new data management software are constantly being developed.
To stay ahead of the curve and show interviewers that you know your stuff, it’s also important to stay up-to-date with the latest trends in data engineering.
A great way to do this is by following data engineering blogs, YouTube Channels, and forums.
Some of my personal favorites are:
- Data Elixir
- Seattle Data Guy
- Reddit – /r/dataengineering
By following these blogs and forums, you’ll be sure to stay up-to-date with the latest data engineering trends, which can be useful when trying to impress your interviewer!
As for technical interview prep, an underrated resource I found to be really useful was Stratascratch.
Stratascratch is an online interview platform that’s great for practicing your technical database questions from big tech companies. Data resources like these are a good place to start practicing.
If you’re looking for a great place to start practicing common technical questions to land a data engineering role, you can start with their free plan!
With these interview tips, I’m sure you’ll get the hang of the interview process really quickly!
Step 6: Get Real-World Experience
Once you are experienced in your skills and are constantly interviewing with companies for data engineering, you’ll want to get some real-world experience.
There are a few ways to go about this:
- Do an internship at a company in the data engineering field
- Work on a data engineering project with a non-profit
- Get involved in the open-source community
All of these options will help you gain the experience you need to get hired as a data engineer.
These varied experiences might encourage you to learning other programming languages as well, which can complement your portfolio perfectly.
Step 7: Land Your Dream Data Engineer Job!
The final step is to land your dream job as a data engineer or big data engineer.
To do this, you’ll need to:
- Build a strong resume
- Network with people in the industry
- Apply to data engineering jobs
- Ace your interviews
If you follow these steps and put in the hard work, you’ll be well on your way to becoming a data engineer!
Is data engineering a good career?
Data engineering is a good career. Data engineering is a rapidly growing field within data science due to the rising demand for data warehousing skills, data management technology skills, and big data tools.
Is it hard to become a data engineer?
It is not hard to become a data engineer. Becoming a data engineer does not require a degree and can be self-taught. However, the effort it takes to learn data engineering does require a lot of hard work and dedication.
How much do data engineers make?
Data engineer salary range around $116,000 per year. However, salaries can vary depending on experience and location.
How do I start a career in data engineering?
The best way to start a career in data engineering is to work on data engineering projects. These data engineering projects can be personal or with a non-profit, or getting involved in the open-source community. These will be useful experiences for landing data engineer jobs.
How long does it take to become a data engineer?
It will take 1-2 years to become a data engineer. Becoming a data engineer requires extensive technical training in data science and data software engineering. However, this can vary depending on previous tech experience, hours dedicated to learning, and location.
Becoming a data engineer can be a challenging but rewarding process. If you follow the steps in this guide, you’ll be on your way to landing your dream job as a data engineer.
The most important thing is to work on projects to display your practical skills, stay up-to-date with the latest trends in the field and practice your interview skills.
With a little hard work, you can become a data engineer in no time! Thanks for reading and good luck!
My Favorite Learning Resources:
My Recommended Learning Platforms!
|Learning Platform||What’s Good About the Platform?|
|1||Coursera||Certificates are offered by popular learning institutes and companies like Google & IBM|
|2||DataCamp||Comes with an integrated coding platform, great for beginners!|
|3||Pluralsight||Strong focus on data skills, taught by industry experts|
|4||Stratascratch||Learn faster by doing real interview coding practices for data science|
|5||Udacity||High-quality, comprehensive courses|
My Recommended Online Courses + Books!
|1||Data Analytics||Google Data Analytics Professional Certificate||–|
|2||Data Science||IBM Data Science Professional Certificate||–|
|3||Excel||Excel Skills for Business Specialization||–|
|4||Python||Python for Everybody Specialization||Python for Data Analysis|
|5||SQL||Introduction to SQL||SQL: The Ultimate Beginners Guide: Learn SQL Today|
|6||Tableau||Data Visualization with Tableau||Practical Tableau|
|7||Power BI||Getting Started with Power BI Desktop||Beginning Microsoft Power BI|
|8||R Programming||Data Science: Foundations using R Specialization||Learning R|
|9||Data Visualization||–||Big Book of Dashboards|