How Do You Become a Data Engineer?

Interested in becoming a data engineer? Springboard can help! Explore everything you need to know about becoming a data engineer in this guide.

Data engineering is a type of software engineering that focuses deeply on data—namely, data workflows, pipelines, and the ETL process (Extract, Transform, Load).

With the rise of big data and its increasingly central role in business priorities, the demand for data engineers has soared. Job growth for data engineers reached 50% in 2019, according to DICE’s 2020 Tech Jobs Report. Demand for data engineers is strongest in industries that have historically leaned heavily on data, including IT, Internet, insurance, financial services and hospitals, and healthcare.

Because data engineering is a relatively new profession that intersects both software engineering and data science, there are no clear-cut steps to become a data engineer.

But that doesn’t mean it can’t be done. This guide will show you all the necessary skills, knowledge, and education you need to become a successful data engineer—all without a bachelor's degree or master's degree in computer science or a related field.

springboard data engineer

What Is a Data Engineer and What Does a Data Engineer Do?

Data engineers are in charge of the delivery, storage, and processing of data. A data engineer’s job is to provide a reliable infrastructure for these functions. Data engineers do this by building data pipelines that transform and transport data from various data sources (such as a CRM system) to a storage system such as a data warehouse. These pipelines enable raw data to be converted into an analyzable format to be used in data science projects.

Put simply, data engineers help build data warehouses and form the crucial yet often overlooked backbone of any data science operation within an organization.

What Is the Difference Between a Data Engineer and a Data Scientist?

The difference between a data engineer and a data scientist is that data engineers focus on the infrastructure needed for data analysis, while data scientists perform data mining and data analysis functions.

If you’re unclear on the distinction between a data scientist and a data engineer, remember:

  • A data engineer is in charge of creating and maintaining data workflows and its underlying infrastructure
  • A data scientist is more involved in manipulating the data itself

data scientist vs. data engineer

How Do You Become a Data Engineer?

Follow these general guidelines to acquire the skills you’ll need to become a data engineer and land an entry-level job.

  1. Learn the right programming languages. Remember that a data engineer is first and foremost a software engineer who also possesses skills in data analysis and statistics. Start by firming up your programming skills and learning programming languages used by data engineers. SQL is the top programming language used by data engineers for creating and managing relational databases. Then, move on to programming languages for statistical analysis and modeling, such as Python or R. Alongside these foundational skills, build your understanding of how these programming languages are applied in the real world.
  2. Learn automation and scripting. Many of the tasks associated with transforming and analyzing data can be automated, especially if the task is repetitive and takes a long time. To automate tasks, you need to know scripting language syntax and operations, and product configurations such as workflow processes, escalations, and actions. Scripting languages can be used to automate certain tasks in a program or to extract information from a data set.
  3. Learn how databases work. Data engineers work with databases containing structured and unstructured data. Relational databases are essentially tables consisting of rows and columns of structured data. Data engineers use SQL to transform and transport data from a data source (such as a relational database) to a data warehouse using ETL pipelines. They also tune databases for fast analysis and create table schemas. Meanwhile, unstructured data is stored in a NoSQL database in the form of documents. Querying a NoSQL database requires a proprietary language that is very different from SQL.
  4. Learn how data processing works. Data processing is the conversion of raw data into an analyzable form. The most commonly used engine for parallel data processing—which is useful for large datasets—is Apache Spark. This data processing framework uses batch processing, which involves collecting data points that are grouped together within a specific time interval. Stream processing deals with continuous data collection in real-time. Each model has different use cases; batch processing is better when you don’t need real-time data, whereas stream processing is essential for keeping business intelligence up to date.
  5. Learn cloud computing. The main advantage of cloud platforms is they centralize processing power and enable companies to store virtually unlimited amounts of data without the associated costs of on-premise storage solutions. The most popular cloud platforms are Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Some job descriptions require familiarity with a specific platform. Cloud platforms provide a range of services that are useful to data engineers, including the ability to use MPP databases that run across several machines and use parallel processing to do otherwise expensive data queries.
  6. Build a portfolio. Think of problems that can be solved by data science, and what data sources and pipelines will be needed to query the data. Predicting oil demand (and pricing), election outcomes, iceberg paths, and the reproduction rates of animal populations are examples of real-world problems that can be tackled using data science. Choose a discipline that matters to you, such as the environment or government policy, and formulate a problem statement. Next, determine what datasets you’ll need and find out if they’re publicly accessible. Gather the data and build a pipeline that enables you to store and query that data.

Data Engineering Job Profile, Skills, & More

Data engineers are responsible for expanding and optimizing data and data pipeline architecture and optimizing the data flow and collection for cross-functional teams. Training to become a data engineer requires a number of specialized skills.

The job description of a data engineer usually contains clues on what programming languages a data engineer needs to know, the company’s preferred data storage solutions, and some context on the teams the data engineer will work with. The level of skills and the foundational knowledge required varies widely from junior data engineering job descriptions to senior data engineering job descriptions.

Learn more about a typical data engineer job description here.

What Are the Top Industries for Data Engineers?

Naturally, the top industries for data engineers are those with a high concentration of data-driven organizations. This includes industries such as IT, computer software, financial services, hospitals, and healthcare.

  • The Data Science Hierarchy of Needs (below) shows the steps an organization must take to use big data in making business decisions. Data engineering fits in row 2 (move/store) and row 3 (explore/transform).
  • The other steps are typically overseen by data scientists, data analysts, and business intelligence specialists.

data science hierarchy of needs

How Can Springboard Help You Become a Data Engineer?

Want to know how to get into data engineering? Springboard’s data engineering online courses and bootcamps are comprehensive, accessible, and come with a six-month job guarantee.

You’ll receive weekly one-on-one mentorship from industry professionals, career services, and the chance to work on real-world projects.

  1. Enroll in one of Springboard’s related career tracks. Springboard offers a number of career tracks that can help you acquire the skills you need to become a data engineer.
  2. Software Engineering Career Track. Become a full-stack web developer in nine months by mastering front-end development (HTML, CSS, JS), back-end development (Python, Flask, and SQL), databases, data structures, and algorithms. Modules include learning resources, practice exercises, projects, and career-related coursework. You’ll also build two full-stack capstone projects to showcase to employers.
  3. Data Science Career Track. In this six-month course, you’ll learn how to program with Python to write clear, elegant code, use Pandas to wrangle and clean data, conduct exploratory data analysis, and learn software engineering and advanced machine learning specifically for data science. You’ll also get the chance to work on 14 real-world projects and build a data science portfolio.
  4. Data Engineering Career Track. Work with a one-on-one mentor to learn key aspects of data engineering and database management including designing, building, and maintaining scalable data pipelines, working with the ETL framework and large data sets, and learning key data engineering tools like MapReduce, Apache Hadoop, and Spark. You’ll also complete two capstone projects focused on real-world data engineering problems and business needs that you can showcase in job interviews.
  5. Build a unique portfolio of projects while being guided by a personal mentor. You’ll work on at least one capstone project where you apply the skills you’ve learned towards building a large-scale project you can showcase to future employers. Projects are focused on realistic scenarios you’ll encounter in a business setting.
  6. Work 1:1 with a mentor. No matter which career track you participate in, you’ll be paired up with a mentor, a seasoned professional who works in the industry you’re interested in. Discuss weekly project deliverables, industry topics, or career advice, and learn how to master and apply key techniques.
  7. Get the perfect job with 1-on-1 unlimited career coaching. Career-focused course material is paired with personal coaching calls to help you land your dream job. You’ll have six scheduled calls, with unlimited access to more. And full career support continues for six months after completing the program.

Data Engineering Career FAQs

Below are some frequently asked questions from people interested in becoming data engineers.

Can you become a data engineer without a bachelor's or master's degree?

Since there is no set university curriculum specifically for data engineering, it is still possible to become a data engineer without a degree.

  • Becoming a data engineer starts with being a good software engineer, so if you choose not to obtain a degree, get certified as a software engineer through an online bootcamp or course, and gain work experience as a developer.
  • Once you’ve proven yourself, start learning about distributed systems, data analysis, and basic machine learning. You might find it useful to enroll in a secondary online bootcamp focused on data science or data engineering and reach your goal without having a degree.

What do you need to know to be a data engineer?

Data engineers need to have a strong background in software engineering and data storage best practices. Skilled data engineers also need to be fluent in the most common programming languages used in data science. Beyond that, you need to understand basic statistical analysis, machine learning, and database architectures. Most importantly, you must know how to build data infrastructure and pipelines using ETL (Extract, Transfer, Load) workflows.

Can a data engineer become a data scientist?

It’s absolutely doable, but the skillsets for each role vary. While data engineering is grounded in software engineering, data science requires strong skills in mathematics and statistics.

  • The programming languages used by data scientists and data engineers are very similar—Python, R, SQL, etc— but you’ll need to learn how to use software libraries used specifically for data manipulation and analysis, such as Pandas, which is written for Python.
  • You’ll need to pick other data analytics skills such as mining and analyzing data, using predictive modeling, developing A/B testing frameworks, and developing custom data models and techniques.

Ready to switch careers to data engineering?

Data engineering is currently one of tech’s fastest-growing sectors. Data engineers enjoy high job satisfaction, varied creative challenges, and a chance to work with ever-evolving technologies. Springboard now offers a comprehensive data engineering bootcamp.

You’ll work with a one-on-one mentor to learn key aspects of data engineering, including designing, building, and maintaining scalable data pipelines, working with the ETL framework, and learning key data engineering tools like MapReduce, Apache Hadoop, and Spark. You’ll also complete two capstone projects focused on real-world data engineering problems that you can showcase in job interviews.

Check out Springboard's Data Engineering Career Track to see if you qualify.

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