Data engineering is one of the fastest growing career fields in data science, and as a result, companies are paying more than ever to hire the best talent. In fact, wage growth has been so rapid that Data Engineers now have some of the highest starting salaries in tech and data science.
Two trends can explain why salaries are going up so quickly for Data Engineers: A talent shortage and a growing opportunity pool.
It’s not hard to see how salaries are linked to these two trends. First, as companies compete for the finite pool of talent, those with the right skillset can command ever higher wages.
Second, more companies are embracing digital transformation and remote work. As a result, the pool of opportunities for Data Engineers continues to expand, beyond the traditional areas of hiring.
Here’s a look at the latest salaries for Data Engineers:
Average Base Salary
Average Total Compensation
Seniority can increase the pay of a Data Engineer. Here are the base salaries of a Data Engineer grouped into 6 seniority categories.
One of the fastest ways to climb the Data Engineer career ladder is an advanced degree. In fact, many mid- and senior-level Data Engineers hold a master’s or PhD degree. In other words, an advanced degree will make you more competitive, help you build specialized skills, and increase your average base salary.
Data Engineers have a specialized skill set and perform critical job functions, which is why it is one of the highest paying jobs in data science. Data Engineers perform a variety of key functions for businesses, often including:
These functions require in-demand skills like ETL, Python, SQL, NoSQL and data warehousing. Machine learning is also helpful to know, although an engineer with advanced knowledge in this field might qualify for a higher-paying machine learning engineer role. Here’s how Data Engineer salaries compare to other data science job titles:
Both seniority and specialization affect salaries for Data Engineers, yet the job remains one of the highest paid in data science. It lags behind only data scientist, machine learning engineer, software engineer and data manager.
Looking for ways to increase your salary in your current role? Without a career switch or pursuing another job title in data science, there are a few ways that Data Engineers can earn more:
Specialization – Specializing in particular tools like ETL or data warehousing solutions, or by mastering engineering for machine learning, a Data Engineer can quickly climb the earnings ladder. Bootcamps offer one of the fastest ways to learn new skills and build mastery.
Training/Education – Education will help you specialize, but it can also provide formal training on advanced concepts, like text mining, data warehousing, ETL and other data engineering solutions. Earning a master’s, for example, can help you jump in earnings; if you have experience, a master’s degree could help you increase earnings from entry-level to mid-level.
Management – The career path for Data Engineers offers plenty of room to grow as an individual contributor if you want to stay on that path; engineers can start in junior level positions and work their way up to Senior Data Engineer. But if you aren’t married to being an IC, management or even executive-level roles offer a lucrative path forward.
Adding Skills – Adding additional skills will help your employability and earnings. For example, a full-stack Data Engineer-e.g. professionals with data engineering and data science skills-can help you command higher wages. Advancing into data architecture, in which you build the blueprint for the engineering department, is another option that requires a strong basis in data engineering.
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