Companies are collecting data at unbelievable rates. According to Forbes, it is projected that companies will add 35 trillion gigabytes of stored data in 2020. But, what are businesses doing with that data? Turning to a Big Data Engineer is a companies top solution.
Engineers are able start mining, cleaning, analyzing, and interpreting the data. They are using data to provide insights to help them make informed decisions. Organizations need big data engineers to make that happen.
Although some may use the terms data scientist or data analyst, and big data engineer interchangeably, they are not the same. Data scientists create computer models that, when applied to large amounts of data, can drive strategic decisions.
On the other hand, data analysts organize and analyze data to answer specific business questions. Data engineers develop the infrastructure, including the data pipeline that transfers data to the scientists and analysts. All three positions are part of the field of data science, which is one of the fastest-growing and highest-paid in technology.
What Does a Big Data Engineer do?
A big data engineer and a data engineer are different titles for the same job, although some people still view them as separate positions.
For people who do separate the two, big data engineers are individuals who use open-source distributed platforms such as Hadoop and data engineers are people who work on data pipelines.
Data engineers rarely create models or analyze data. Instead, data engineers work behind the scenes to create a solid foundation for other data science professionals. A big data engineer may perform any of the following:
- Extract data from multiple sources
- Evaluate, parse and clean data sets
- Build data pipelines
- Merge data sets
- Write programs to extract, transform and load data sets
- Create data stores
- Implement frameworks
As a data engineer, you will be working behind the scenes to ensure the best possible outcomes.
What Skills are Needed?
Data engineering, like many of the newer technology fields, has no clear path to landing a job.
It is not the degree that matters as much as the skills and experience. The following five skills are what recruiters are looking for in a data engineer:
- Apache Spark, which is essential for data analytics
- Machine learning that is used in predictive analytics
- NoSQL experience because SQL databases are being replaced
- Apache Hadoop since it is still used in a significant number of enterprises
- Cloud clusters that are needed to accommodate the volume of data
Beyond the specific skills a data engineer should be familiar with:
- Relational and non-relational databases
- Data modeling techniques
- Database clustering
- Data architecture
These skills must be coupled with experience that illustrates the ability to apply the skills in a real-world environment.
How to Become a Big Data Engineer?
To become a big data engineer, taking control of your career path is a must.
Although Europe has programs for data engineers, the United States does not. For those who choose a degree, their options are a major in computer science or information technology.
Even then, a student will need to make sure that the program offers courses in the core data engineering skills. Pursuing a degree is not the only option. Like many of the positions in the field of data science, how you acquire the expertise is not as important as that you have it.
If you want to become a big data engineer, you need to create a personalized road map, where you identify the required skills and determine the best method to acquire them. Look at boot camps, workshops, online classes, and internships. In most instances, a data engineer is not an entry-level position because employers prefer experienced candidates. That’s why a mentorship or internship program is so beneficial.
Some data engineers come from a different area of data science or engineering. Many start as a developer if their experience is in building tools, infrastructures, and frameworks. Most people start as junior data engineers, working their way up to senior or lead big data engineers. Leverage your skill set to work on projects that can show what you can do.
How Much Does a Data Engineer Make?
The average data engineer makes around $91,000, although salaries can range from $64,000 to over $130,000. Of companies currently looking for big data engineers, tech companies such as Amazon and Facebook pay the highest salaries. The financial services sector pays the lowest. Fidelity Investment, for example, is paying less than $70,000 per year.
Data engineers are in high demand. According to Burning Glass, data engineering remains the top technology job with an 88.3% increase in demand over the last 12 months.
The skills in demand based upon salary are — experience with Apache Spark and extract, transfer and load design. Data analysis and SQL skills are the least in demand and pay the lowest. Keep this in mind as you develop your big data engineer road map.
Should You Become a Big Data Engineer?
That depends. Do you enjoy building things? Do you like to create something out of nothing?
Are you happy to stay in the background and let others be the “rockstars” of data science? Is your answer YES? Then, a big data engineer is perfect for you.
Big data engineers build the house that the rest of the data science professionals live in. People may focus on the interior design of the house and forget about the construction. They may not see the creative use of space or the strong foundation as they tour the home. But, without that framework, the house collapses.
If building excites you, then take control of your life and start down the path to become a big data engineer. With Powered by Woz U programs, you can have a job you love in no time.