Big data is growing fast. According to IBM, 2.5 quintillion bytes of data are generated every single day. In fact, big data is growing faster than companies can keep up with it. Last year, the CTO of IBM said that only 1 out of every 10 big data projects gets released into production.
What this shows is a disconnect between the vast amounts of available data and the people with the skills to analyze that data. Enter the data engineer.
What is a Data Engineer?
Data engineers are engineers who build out, maintain, and improve data pipelines. They design systems to mine and acquire data, and are experts in large-scale data processing and storage solutions.
Not to be confused with data scientists, who develop algorithms to analyze data to solve business solutions. Data engineers are essentially the bridge between the data scientist and the data they’ll analyze. Without the data engineer, data scientists would not be able to do their job.
Essential Skills of a Data Engineer
Since data engineers enable data scientists to do their work, it’s important that data engineers have a thorough understanding of data science. They should understand how the data is analyzed in order to build out the right tools for data scientists to use.
Additionally, it’s essential for data engineers to have solid skills in:
- Databases (SQL, Cassandra, etc) and database management
- Programming and scripting (Python, R, Ruby, etc)
- Hadoop-based analytics (HBase, Pig, MapReduce, etc)
- Distributed systems
Day in the Life of an Entry Level Data Engineer
Entry-level data engineers should expect most of their daily activities to be similar to any entry-level engineering job: fixing bugs. Data engineers work on creating and maintaining data pipelines, which function similarly to any other technology in that these pipelines must be constantly tested and tweaked.
Entry-level data engineers work under the guidance of senior-level data engineers to maintain these systems. Through fixing bugs and eventually adding small features to these data systems, entry-level data engineers learn the skills they need to develop their career into more senior work, like designing and creating these systems.
Expected Salary of a Data Engineer
According to Glassdoor, entry-level data engineers make over $85,000 a year — a solid salary! On average, data engineers make $105,000 across the U.S. Data engineers located in San Francisco make 23% higher than data engineers in other parts of the country, coming to just over $125,000 a year. Even entry-level data engineers make more money in San Francisco, with salaries averaging $107,000 a year.
How to Become a Data Engineer?
If becoming a data engineer sounds fascinating to you, there’s never been a better time. LinkedIn’s 2020 Emerging Jobs Report placed data engineering as No.8 on their list of top 15 emerging jobs and noted that this field has grown by 33% in the last five years.
Switching into the field from another job is also totally normal. Many data engineers come from IT, DevOps, and other engineering backgrounds, though a degree in tech is not a prerequisite.
To get started on your data engineer learning today, you can begin Udacity’s Data Engineer Nanodegree program. At 10 hours (or less) a week, you’ll be ready for an entry-level data engineering position in less than six months.