Case Studies

This post is contributed byBurtch Works’ data engineering recruiting team.

As I wrote about last year, the expansion of data teams means that, increasingly, data engineers are working alongside data scientists to prepare data for analysis and put data products into production.Being a recruiter that specializes in data engineering roles, I often get a lot of questions from employers and professionals alike about the plethora of job titles and specializations that are emerging in the profession. I addressed some of the most common data engineering titles in a separate blog, but I also wanted to talk a little bit about how data engineers differ from data scientists.

The Typical Data Engineer Background

• Bachelor’s/Master’s (sometimes PhD) in Computer Science, Computer Engineering, Information Systems, or Information Technology
• Languages: Java, Scala, and Python (to name a few of the main ones)
• Programming to build products
• Specializes in distributed systems, often including cloud technologies
• Builds complex/highly scalable data pipelines – expert at picking tools/technologies (sometimes 15+ technologies) in order to build the pipeline
• Builds Big Data frameworks, systems, and platforms
• Puts code into production – can rewrite Python/R code to Java/Scala
• Understands data science concepts and principals and may do some analysis but not at the same level as a Data Scientist
• Doesn’t necessarily need to have domain experience
• Works with structured and unstructured data

The Typical Data Scientist Background

• Master’s/PhD in Math, Statistics, Computer Science, Engineering or Physics
• Often come from academic research background, with a strong foundation in Math, Statistics, or Physics
• Languages: Python, R, SQL, and SAS
• Programming in order to analyze data – coding skills usually are not at the same level as a Data Engineer because they don’t need to be
• Develops statistical models
• Responsible for deriving insights and prescribing action from data
• Need to have domain experience to fully contextualize their data/analysis
• Works with structured and unstructured data

Although data scientists may also be adept at data acquisition or cleaning/transformation, the skill that often differentiates them from data engineers is their ability to analyze data and develop insights from the data.Burtch Works produces salary reports on the data science profession (which can be downloaded here) that contain a more complete definition of how we define this area, but you can also read a short summary of that definition on our blog.

The Main Difference Between Data Engineers and Data Scientists

There are many similarities and often skill overlap between data engineers and data scientist. However, the main differences are that data engineers’ skills will lean more towards programming and software engineering to build highly scalable data products, while data scientists will focus more on advanced statistical analysis to pull insights out of the data and bring value to the business.We hope you found this information interesting, and if you’re looking for opportunities or to hire professionals in data engineering, be sure to connect with us.