Data engineering services often focus on two main parts of the data platform: pipelines and platforms (infrastructure). An engineer with the full-stack title will do both. Below are some typical roles of big data engineers:

Pipelines:

• ETL - Typically requested by the analytics team. For example, an analyst needed data from November to December 2022 for analysis but could not locate it in Data lake. The data engineer will find the source, which could be the Backend Database or a third-party API, and then sync it to the Data lake for the analyst to use.
• Data API. It functions similarly to a backend application. Engineers will create a Spark Job to retrieve data from the Data lake, process it as needed, and save the results in the database (Mysql, Postgres, Elastic Search). Finally, they create an additional Rest API to trigger Spark Jobs.
• Streaming data. The name is self-explanatory. Freelance data engineering developers will get real-time data from Kafka and feed it into the Data lake.

Platforms:

• Set up the operating system and keep Airflow running to load over 2000 tasks. Furthermore, because the system supports numerous teams, both technical and non-technical, engineers must configure CI/CD so that everyone can use Airflow easily.
• Build and maintain a data warehouse or "data lake". It is the most fundamental project that any data engineer must complete.
• People can handle a small quantity of data comfortably on their personal computers. But for an enterprise, especially those with strict security regulations and measuring data in Petabytes, a data engineer is needed to develop a separate system to handle this efficiently. Data engineering services also build other platforms such as Kafka, CI/CD, Hive, Presto, etc.