A Data Engineer is responsible for designing, building, and maintaining the infrastructure and systems that are needed to collect, store, and process large amounts of data. This typically includes tasks such as designing and implementing data pipelines, setting up and managing data storage systems, and ensuring that data is accurate and accessible to those who need it. Data Engineers work closely with Data Scientists and other data professionals to ensure that the data they are working with is of high quality and can be used to answer important business questions.
What’s the difference between a data scientist and a data engineer?
- Data Scientist and a Data Engineer have different roles and responsibilities within an organization.
- A Data Scientist primarily focuses on analyzing and interpreting complex data sets, developing and implementing machine learning models, and using statistical and mathematical techniques to extract insights and knowledge from data. They also use data visualization tools to present findings and communicate with stakeholders.
- On the other hand, a Data Engineer focuses on the design, construction, and maintenance of the systems and infrastructure that are needed to store, process, and manage large amounts of data. This includes tasks such as creating and maintaining data pipelines, designing and implementing data storage systems, and ensuring that data is accurate and accessible to those who need it.
In summary, a Data Scientist is more focused on the analysis and interpretation of data, while a Data Engineer is more focused on the infrastructure and systems that support data analysis.
What are the other names for data engineers?
Data Engineers are also known by several other names, including:
- Data Infrastructure Engineer
- Big Data Engineer
- Data Platform Engineer
- Data Pipelines Engineer
- Data Architecture Engineer
- Data Integration Engineer
- Data Management Engineer
- Data Warehousing Engineer
- Data Operations Engineer
These names reflect the various aspects of the role, such as building and maintaining data infrastructure, working with big data technologies, designing and implementing data pipelines, creating data architectures, integrating and managing data, and ensuring data availability and accessibility.
What’s a typical salary for data engineer?
The salary for a data engineer can vary depending on factors such as location, experience, and the specific company or industry.
In the United States, the average salary for a data engineer is around $120,000 per year, according to data from Tarta.ai. This can range from around $90,000 to $160,000 or more, depending on factors such as experience, location, and the size and industry of the employer.
It’s also worth noting that the salary for data engineers can vary depending on the specific skills and experience required for the role. For example, a data engineer with experience in a specific big data technology like Hadoop or Spark may command a higher salary than one with a more general skill set.
It’s important to consider that the salary range can also vary based on region, some cities like San Francisco, Seattle, New York, and Boston tend to have a higher median salary for data engineers compared to other cities in the US.