Data Engineer vs Data Scientist: Roles and Responsibilities
At first sight, data engineers and data scientists’ job roles are similar. They are both programmers and deal with data sets, but the data engineer builds, tests, and maintains data ecosystems. The data scientist’s main role is to analyze data to build prediction algorithms.
Data engineer skills: programming, mathematics, big data.
Scope of work:
- Data acquisition
- Develop data set processes
- Algorithm creation
- Data storage
- Deploy sophisticated analytics programs, ML models and algorithms
Data scientists skills: mathematics, programming, communication.
Scope of work:
- Identifying hidden patterns in data sets
- Transferring data into a new format to make it more appropriate for analysis
- Cleansing and collecting quality data to feed to train algorithms
- Data visualization
- Building tools to automate data collection
Data Engineer vs. Data Scientist: Salaries
According to PayScale:
Data Engineer: $65K — $132K
Data Scientist: $67K — $134K
According to Glassdoor:
Data Engineer: $72K- $158K
Data Scientist: $83K-$154K
If company leadership does not understand the difference between data engineer and data scientist roles and responsibilities, it may cause teams to fail or underperform big data.
In our full article, we have compared their roles based on our experience. Go further to learn more about it using the link below.
