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The differences between data specialists and data engineers can be seen in a few details. Each job has different responsibilities and duties. While it typically takes 1-2 years to become a data specialist, becoming a data engineer takes usually requires 2-4 years. Additionally, a data engineer has an average salary of $109,675, which is higher than the $68,326 average annual salary of a data specialist.
The top three skills for a data specialist include data analysis, data entry and data collection. The most important skills for a data engineer are python, java, and cloud.
| Data Specialist | Data Engineer | |
| Yearly salary | $68,326 | $109,675 |
| Hourly rate | $32.85 | $52.73 |
| Growth rate | 9% | 21% |
| Number of jobs | 100,301 | 303,105 |
| Job satisfaction | - | - |
| Most common degree | Bachelor's Degree, 56% | Bachelor's Degree, 65% |
| Average age | 44 | 39 |
| Years of experience | 2 | 4 |
A data specialist's role is to process data, transferring them into an electronic platform or database for record-keeping or creating systems. Their primary responsibility is to ensure the accuracy of every inputted data point and verify its authenticity by reaching out to clients or using specific software. There are also instances when they must perform various analyses or take part in different product development processes. Furthermore, it is crucial to be able to identify any anomalies or inconsistencies; this way, corrective measures can be quickly implemented.
A data engineer is someone who makes data science possible. This IT job requires the search for data set trends and algorithm development to make raw data more beneficial to the enterprise. Data engineers are responsible for establishing and maintaining an environment that permits other data functions. The necessary skills for the job include in-depth knowledge of multiple programming languages and SQL database design. Among the other skills data engineers should develop include data warehousing and architecture, data mining and modeling, and statistical regression analysis.
Data specialists and data engineers have different pay scales, as shown below.
| Data Specialist | Data Engineer | |
| Average salary | $68,326 | $109,675 |
| Salary range | Between $40,000 And $116,000 | Between $80,000 And $149,000 |
| Highest paying City | San Francisco, CA | San Francisco, CA |
| Highest paying state | Connecticut | California |
| Best paying company | McKinsey & Company Inc | The Citadel |
| Best paying industry | Manufacturing | Technology |
There are a few differences between a data specialist and a data engineer in terms of educational background:
| Data Specialist | Data Engineer | |
| Most common degree | Bachelor's Degree, 56% | Bachelor's Degree, 65% |
| Most common major | Business | Computer Science |
| Most common college | - | California State University - Long Beach |
Here are the differences between data specialists' and data engineers' demographics:
| Data Specialist | Data Engineer | |
| Average age | 44 | 39 |
| Gender ratio | Male, 40.6% Female, 59.4% | Male, 81.5% Female, 18.5% |
| Race ratio | Black or African American, 9.6% Unknown, 4.3% Hispanic or Latino, 19.5% Asian, 9.9% White, 56.0% American Indian and Alaska Native, 0.6% | Black or African American, 4.3% Unknown, 4.8% Hispanic or Latino, 8.0% Asian, 30.1% White, 52.7% American Indian and Alaska Native, 0.2% |
| LGBT Percentage | 5% | 8% |