How to Become a Data Engineer: The Complete Career Guide
The author of this article is tech expert Pieter Murphy.
In this article
Data engineering as a career path is becoming ever more viable for skilled professionals interested in designing and building the systems we use to make sense of the raw info we collect. Businesses and other organizations have become more reliant on data and continue to innovate to get insights from it.
If you want to get into data engineering as your career, there are some things you need to do to prepare. Start with laying the foundation with a degree, which we discuss below, learn programming, and gain experience in the field, across segments like warehousing and big data technologies.
With the foundational data engineer essential skills, hard work, and dedication, you can build a career with rewarding opportunities and high pay.
The Role of a Data Engineer
As the name implies, you are tasked with designing and building systems for collecting, storing, and analyzing data. Becoming a data engineer has broad applications in almost any industry that collects it.
Organizations need it to be highly accessible and usable by the time it gets to analysts and scientists. That is where an engineer comes in.
Responsibilities of a Data Engineer
Getting started with data engineering begins with understanding what an engineer does. The core responsibilities include:
- Working on architecture — Engineers design, build, maintain, and troubleshoot data architecture to meet business requirements and ensure quality, scalability, and security.
- Collecting and storing findings — You’ll collect and collate findings from multiple sources, including mobile, web, social media, IoT, etc., and store them in the appropriate repositories, warehouses, or lakes.
- Conducting research — You’ll need to research and analyze the industry’s trends and technologies and apply them to improve the solutions and processes.
- Automating tasks — To ensure info availability, reliability, and efficiency, you should learn how to automate pipelines, workflows, and tasks using tools like Apache, Luigi, Airflow, or AWS Glue.
- Improving on current skills — You need to keep learning and updating your knowledge and skills, as the engineering field is fast-changing and evolving and needs you to constantly adapt and innovate.
To master these responsibilities is to be a good data engineer, which opens up more prospects as you grow in your field.
Difference Between Data Engineers, Data Scientists, and Data Analysts
While all these professions may be in the data field, there are differences in what you must do depending on your role. Let’s break it down:
- Data engineer — As mentioned, the engineer designs and builds the systems for collecting, storing, and analyzing facts at scale.
- Scientist — In this role, you collect, analyze, and interpret complex sets of information using statistical and machine-learning techniques.
- Analyst — Analysts are tasked with collecting, organizing, and analyzing data to identify patterns and insights that can be used to make informed decisions.
Specializations in Data Engineering
Being a data engineer opens up opportunities in several specialized fields that you can focus on to further enhance your competitiveness. Some of the major ones include:
Big data engineers have a software engineering background and specialize in working with large and complex information sets using big data technologies like Hadoop, Spark, and MapReduce. They design and build data pipelines, work with scientists to put code into production and use programming languages.
To become a cloud data engineer, you must understand how to use cloud-based services like Google Cloud, AWS, or Azure to create scalable, secure, cost-effective solutions. It is a skill that enhances data engineering capabilities while empowering project flexibility.
Discover additional insights into crafting a career path in cloud computing.
Machine learning engineers have engineering backgrounds but apply ML techniques to data problems. They bridge the gap between data engineering and science and create and deploy models that can provide insights and predictions from data.
Computer Vision Engineer
Under this role, engineers use machine learning and deep learning techniques for image processing and analysis. They create solutions for object detection, face recognition, pattern recognition, and other computer vision tasks.
As an architect, you come into this with a software engineering and DBA background. That allows you to specialize in designing and managing architectures for various systems.
As the field evolves, more roles will emerge that open a path to become a data engineer specialized in your niche for more competitiveness in the jobs market.
Educational Background to Become a Data Engineer
The road map to become a data engineer from scratch starts with understanding the skills and knowledge required. What is the educational background requirement? Let’s explore.
Academic Degree vs Self-education
While some may look at what they should study to become an engineer in formal settings, circumstances may force others to consider self-education options.
To get started in data engineering, you will need a college degree in computer science, data science, statistics, software engineering, mathematics, or a related field. Degrees provide a solid theoretical background that follows a structured curriculum that covers topics and tools of engineering, including programming languages, structures, cloud computing, repositories, algorithms, etc.
With it, you get the opportunity to not only network with peers but also bring a sense of credibility to your abilities. However, a degree to be a data engineer can be expensive, time-consuming, and might not cover the latest trends and technologies in the fast-changing tech world. It is also not enough to demonstrate your skills and experience, which is essential.
The roadmap to become a data engineer without a degree can go the self-education route where other alternatives aren’t available. You can find useful online courses, blogs, books, videos, projects, boot camps, and other resources, which provide a flexible, affordable, and up-to-date way to learn the topics and tools to get into this career.
Self-education also offers more opportunities to put your skills to use, padding your portfolio with valuable experience, which is valuable to potential employers. However, self-education has its challenges, including that it requires discipline, guidance, and self-motivation.
Depending on how you go about it, it may also not provide the credibility a degree does.
In your steps to become a data engineer without a degree, your choice depends on your preferences, goals, and circumstances. The most important thing is having a passion for learning and excelling in engineering.
To get a deeper look into the difference between formal education and self-taught, explore our coverage of the difference between self-taught and degree programmers.
Courses and Certifications
Finishing engineering courses and earning certifications is a great addition to a degree holder or self-educated engineer aspirant. You can find courses and certifications online from top education institutions, companies leading the field, and industry leaders.
Some of the courses you can take to help you become a good data engineer include:
- IBM Data Engineering by IBM
- Meta Database Engineer by Meta
- Data Engineering Foundations by IBM
- Microsoft Azure Data Engineering Associate by Microsoft
- Python, Bash, and SQL Essentials for Data Engineering by Duke University
These courses and more can help those just starting after earning a degree, who are self-taught but want a way to prove their skills, or those looking to upskill or change careers.
Certifications are a great way to prove that you understand a topic by taking and passing a specific exam. Some of the best-known ones you should study to become a data engineer with credibility include:
- IBM Data Engineering Professional Certificate
- Google’s Cloud Data Engineer Professional Certificate
- IBM Data Warehouse Engineer Professional Certificate
- Meta Database Engineer Professional Certificate
Certifications are often offered at the end of courses or separately. Once earned, you can display them on your job-seeking profiles to ensure your application stands out.
Required Skills to Get into Data Engineering
Getting into engineering and succeeding at it takes several skills that encompass technical knowledge and soft skills. A good mix of both ensures you can impress recruiters or potential employees and thrive wherever you find an opportunity.
To get hired as a data engineer, here are the skills you need to develop:
Hard skills are the technical skills you should learn to become a data engineer. They can be earned through study and practice and proven with certifications or completed courses. For a data engineering career path, you will need to develop your skills in:
Engineers must understand coding and the programming languages used, including Python, Ruby, Golang, Perl, Java, Scala, MatLab, R, SAS, C, C++ and more.
You should know how to manage directories. Structured Query Language (SQL) is the most widely used solution, requiring that you know it in-depth. It is highly valuable in your career and must be incorporated into your skillset.
Extract, Transform, and Load is what ETF stands for. One of your main skills is combining facts from different/multiple sources into a large repository (warehouse), which requires understanding the rules of organizing raw info and preparing it for storage, analytics, or use in machine learning and other applications.
Big Data Technologies
To start a career in data engineering, you must be familiar with big data technologies like MapReduce, Spark and Hadoop to process and analyze large and complex information sets in a distributed and parallel manner. These technologies allow you to handle structured, semi-structured, and unstructured data and perform tasks like ETL and querying.
Proficiency in cloud computing platforms like Azure, AWS, or Google Cloud is crucial. The platforms provide reliable and cost-effective solutions, services, tools, and features like storage, warehousing, pipelines, analytics, and machine learning, which engineers can use to build and manage systems.
Understanding ML allows engineers to use concepts like supervised and unsupervised learning, regression, classification, clustering and recommendation systems. Applying ML algorithms and frameworks like TensorFlow, PyTorth, or Scikit-learn helps engineers surface insight from what’s collected.
You should learn to design and implement datum API interfaces to access and exchange information between apps and systems. You should learn to use tools and protocols like REST, GraphQL, or gRPC to create secure, efficient, and user-friendly APIs.
These are not all the skills you need to be a data engineer, but they give you some idea of the technical side of things, what they focus on, and the tools and technologies used.
Soft skills, as the name implies, are less about the technical nature of being a data engineer and more about the cultural/human side of your career. Getting a job as a data engineer will have you in constant communication with other IT experts, stakeholders, and peers in your work environment, virtual or in-person.
Here are the skills you need to navigate that:
As an engineer, you must communicate your work to your peers or other relevant parties and any insights and ideas you may surface from interacting with data. Communicating formally and informally, relevant to context, is a great way to get yourself heard, share ideas, and even lead projects or teams.
Your ability to find solutions to data-related problems, such as quality, integration, performance, and security, can greatly assist your career and growth.
The engineering field is highly collaborative, where you must work with scientists, analysts, developers, and other engineers to share ideas, resources and knowledge. Knowing how to cooperate and coordinate with others while maintaining respect and appreciation for different perspectives is crucial to growing in the career.
- Time management
This might seem like a no-brainer, but the very flexible nature of an engineering role means working on multiple tasks simultaneously with deadlines to keep in mind. The ability to meet those deadlines in the highly competitive field can be the difference between growing and stalling your career.
Data engineering is not just a fast-evolving field that asks engineers to adapt to new technologies, tools, and trends while handling uncertainty and change. The pressure could slow you down if you are not prepared to adapt to the demands of the field.
There are more skills to add, most of which you can intuit from interacting with the industry and engineers in any capacity. Identify what drives your career forward and focus on that for the best results.
Learning Path to Become a Data Engineer
To recap, the data engineer learning path often involves going through the following steps:
- Gain proficiency in programming languages like Python and Scala.
- Learn how to script and automate.
- Gain experience in database management and hone your SQL capabilities
- Learn how data is processed in different contexts
- Create a schedule for your workflow
- Develop your cloud computing knowledge across AWS, Google Cloud, and Azure platforms.
- Learn how to use infrastructure and orchestration tools like Kubernetes and Docker
Above all this, keep yourself up to data with industry trends and technologies, to ensure you can always create the most optimal solutions as part of your role.
Data Engineering Networking
Networking is one of the best ways to open up more opportunities to become an engineer. Not many people start their careers trying to get a job as a data engineer. It usually starts with a background in related fields and gradually moves on to engineering as more organizations leverage what they collect.
With your degree to be a data engineer under your arm and in search of experience, here are some places to start:
- Join online communities on LinkedIn, Reddit, Twitter, Stack Overflow and Quora, where you can follow, comment and share posts from peers, influencers, and experts. You can also join groups relevant to your field to learn from them.
- Attend events and meetups to meet other engineers in person. You can find online or local events and meetups on platforms like Meetup, Eventbrite, or Datacamp to attend workshops, conferences, webinars, hackathons and other activities that interest you.
The roadmap to become a data engineer with no experience is greatly helped by networking, a it gives you more opportunities to work with.
How to Get First Practice in Data Engineering
You have heard the conundrum, you have probably even seen some memes. Employers require that you show some experience before they hire you, sometimes a comical amount of experience.
The question for you then becomes, ‘how can i get a data engineer job without experience?’
Working as an intern is famously known to be grueling, but it is a great way to get real-world experience and learn from professionals, Internships can help you apply your skills and get feedback and guidance on your ability while building your network and portfolio.
You start and work on pet projects for your interest or curiosity. They can be a great way to challenge your creativity and learn new things. They can also use your portfolio to demonstrate your initiative and problem-solving ability.
Open-source projects allow an inexperienced engineer to gain real-world experience among peers and role models. Open-source projects can help you improve your technical skills, learn new tools and technologies and showcase your skills and passion to potential employers.
How to Get a First Job as a Data Engineer
Create a Resume
This standard document introduces who you are and what you can do. It summarizes your education, work experience, skills, and achievements. Here’s where you put your qualifications, abilities and potential fit for the role. A good resume needs to be clear, concise, and tailored to the specific job you apply to.
Create a Portfolio
Your portfolio focuses on documenting all your projects, documents, code, and other things you can use to showcase your engineering skills. The portfolio outlines the practical knowledge, problem-solving abilities, and creativity required to become a data science engineer. You should include relevant, interesting, and impressive projects when applying for a job.
Prepare for Interviews
Before you go in to see recruiters or attend an interview, prepare. A good impression can significantly boost your chances of getting a job, and that comes from preparing. You can prepare by reviewing your data engineering concepts, practicing your coding skills, researching the role and company, and preparing your questions.
Start with Entry Level Roles
Getting into the field, you want is easier if you can get your foot in the door. Often, that means finding a job that you would have no trouble qualifying for and starting there. It will help you hone your skills and qualify for more advanced roles in a gradual progression.
Pathways for Career Growth in Data Engineering
In engineering, you can take your career path in different and varied directions. Some of them include:
- Senior data engineer
- Data engineering manager
- Data architect
- Machine learning engineer
- Data engineer consultant
These are some common pathways, but there are more possibilities depending on your goals, skills, and interests.
Get Started In Data Engineering, With or Without Experience
As the jobs market becomes more democratized, as seen with the rise of freelance work, hiring based on specific proven credentials and experience instead of educational background, and remote work, you can get into the industry with or without experience and grow your skillset right alongside your career.
If you have what it takes, are passionate about data, and find working with it enjoyable, then designing and building the systems used by other professionals everywhere should be more than fulfilling.
Research what options are available to you and find out if this path may lead to your dream career.