If you’re interested in how to become a data engineer, you’re in luck. Data engineers are in high demand, and resources for how to become a data engineer abound!
Data engineers are technical professionals who understand how data analysts and data scientists need data, then build the data pipelines to deliver the right data, in the right format, to the right place. The best data engineers can anticipate the business’s needs, track the rise of new technologies, and maintain a complex and evolving data infrastructure. And with stewardship of today’s most valuable asset – data – data engineers also contribute directly to a company’s innovation and growth.
Aside from the sheer importance of the role, luck comes to data engineers in other ways and for different reasons.
7 Ways Data Engineers Are Lucky
- Chart your own path! As a relatively new discipline, data engineering opportunities abound.
- Data engineers come from diverse backgrounds. They may have started as developers, software engineers, data analysts, or even physics students who learned Python and fell in love with the power of data.
- Data engineers get to put their non-technical skills to work. They communicate across departments to understand business needs and use big picture thinking to plan complex infrastructure systems.
- Data engineering touches every industry. You can do what you love in an area that interests you.
- Data engineers get to be part of solving big problems. Data helps us understand and address everything from climate change to racism to gender equality and beyond.
- Data engineers can find work-life balance, despite the great abundance of work to be done. Finding the right tools that enable self-service data at scale is essential.
- And let’s not forget the cha-ching salary and job opportunities.
So how do you become one of the lucky ones? This St. Paddy’s Day, we share some of the best resources for how to become a data engineer. But first, let’s start with…
Essential Skills for a Data Engineer
To become a data engineer, you’ll want to have these foundational skills under your belt. Most roles call for at least one programming language (legacy ETL and data pipeline frameworks still require hand coding). In job listings, you may run into requests for Java, Scala, R, and others. You can bet on Python as a smart place to start, the top programming language used for statistical analysis and modeling, requested in ~70% of job listings!
And if hand coding’s not your jam, don’t worry. Once you make it in the door, you can convince your company to modernize with a no-code data engineering platform.
Next, you’ll want to gain an understanding of the systems that store data:
- Databases (SQL and NoSQL)
- Cloud data warehouses (Amazon Redshift, Azure Synapse, Google BigQuery, Snowflake Data Cloud, etc.)
- Cloud data lakes (Amazon S3, Azure Data Lake Storage, Google Cloud Storage, Databricks, etc.)
To start with SQL, the standard programming language for building and managing relational database systems, try this free intro to SQL course offered by Khan Academy.
Data resides everywhere these days and APIs, most commonly REST-based, facilitate connections between systems for the collection and ingestion process. So, make sure you understand how to use APIs.
Now that you know what you need to know, the next section tackles how to gain those skills.
How to Become a Data Engineer if You’re Starting from Scratch
You may hear you need a background in engineering or data science/analysis to become a data engineer. While ideal, options exist for those with other backgrounds. Look for a program that guides your path.
Data Engineering Bootcamps
Bootcamps have popped up as a popular alternative to full computer science degrees for a wide range of technical disciplines. You’ll find coding bootcamps, UX/UI bootcamps, data science bootcamps, and – yes – now data engineering bootcamps.
Springboard offers a 6-month data engineering bootcamp with an industry-driven curriculum and mentor-guided learning. You’ll learn the technologies you need to understand (Python, Hadoop, Kafka, Kubernetes, and more), alongside the theory you need to put it to work. Modules include big data engineering, data engineering in the cloud, data pipelines and orchestration, and streaming data and APIs.
Udacity falls somewhere between bootcamp and MOOC (Massive Online Open Courses), with a strong focus on job training. They offer a data engineering ‘nanodegree’ where students learn data modeling, cloud data warehouses, how to build a data lake with Apache Spark, and data pipelines with Apache Airflow.
How to Become a Data Engineer if You Have an Engineering or Data Background
With experience in engineering or as a data analyst, you will likely need less structure. Start with a Google search to find out how others in your situation went about transitioning to a data engineering role. There are lots of stories out there on blogs and forums.
Data Engineering Blogs and Forums
MOOCs like Khan Academy and more let you learn at your own pace, free. Coursera has several data engineering courses offered by both vendors and universities. Many of these courses have 4-5 out of 5 stars, with 10s of thousands of ratings. You can learn everything from coding Python to data engineering with Google Cloud and beyond. Fair warning: you may end up down a rabbit hole while searching for the right course!
At StreamSets, our mission is to make data engineers wildly successful. Check out our DataOps Platform or pop into a session of StreamSets Live: Demos with Dash. See how easy it can be (hint: no coding involved) to build pipelines and ingest data from multiple sources to any cloud platform. If you’re a StreamSets customer, think about StreamSets Certification.
Start making your own luck this St. Patrick’s Day. Pick whichever method of learning fits your situation or style, and move toward an even luckier future. Sláinte!