The roles of “data engineer” and “data scientist” are often conflated and it’s common practice, but they’re actually completely different jobs.
Both require a high degree of skill in data science, but they each have their own specific responsibilities.
What is Data Science?
Data science can be defined as the process of collecting and processing enormous amounts of data in order to find patterns or trends. The results from big-data processing are used by companies to find insights that help them make better business decisions.
Other use cases for data science include:
- Design teams use data science to help make their graphics and interfaces more user-friendly.
- Product teams use data science to evolve their products to the needs of their customer.
- Marketing teams use data science to learn more about how to reach their customer market.
Data Scientists in Action
Data scientists are the people who analyze data, create algorithms and make predictions based on that data. This role is a step beyond a data analyst, who spends their time cleaning and organizing data. Data scientists spend the majority of their time forming, testing and tweaking algorithms for machine learning.
Their skill sets include statistics, programming (typically Python or R), machine learning algorithms, data visualization and big data. Usually data scientists will use at least one kind of software to do their work: Hadoop, Matlab, Excel, Tableau, etc.
According to Glassdoor, the average pay for a data scientist is $140,000 a year, with even the lower end of the range coming in over six figures.
Data Engineers in Action
Data engineers build the pipelines that collect and deliver data for data scientists. The role is very different in that they’re focused specifically on designing systems and supporting data science work more than actually analyzing data.
That being said, it’s also critical for data engineers to have a thorough understanding of the work that data scientists do so that they can deliver the best possible data pipelines.
Their skill sets include databases (SQL/NoSQL), programming, distributed systems, ETL tools, data APIs and data structures. Data engineers typically use a variety of tools to do their work, including shell languages, Docker, CI/CD (like Jenkins), SQL GUIs, DataWarehousing (like Redshift), cloud computing (like AWS), etc.
According to Glassdoor, the average pay for a data engineer is $138,000 a year, but just like for data scientists, the lowest pay in the range is still above six figures.
Do You Want to Be a Data Scientist or Data Engineer?
Are you interested in helping companies discover solutions to their problems using data? Does the idea of uncovering valuable insights with technology get you excited about your career?
There’s never been a better time to change careers into the field of data. Whether it’s data engineering or data science, both careers are growing fast. According to LinkedIn’s 2020 Emerging Jobs Report, data scientists are #3 on their list of top 15 emerging jobs and data engineers are #8. Plus, both roles have grown over 30% in the last five years, which is significantly faster than normal.