You need to know SQL and Python very well to find a data engineering job. Experience with a workflow management platform like Apache Airflow can help you build ETL pipelines and have this skill in your toolbox. In addition, you need to know the fundamentals of Hadoop, Hive, Spark, and data warehousing to pass the interviews and be able to do your daily job. For many companies and roles, knowing too many details about Hadoop, Hive, or Spark is unnecessary since many DE roles interact with data warehouses through tools like Presto or Hive, which are SQL-based.
What skills are important to land a Data Engineering job at a well-known tech company?
Many people think they need to be fluent in Spark or know everything about Hadoop systems to get a job with these companies. Although it is true for some data engineering roles, it is not relevant to many other data engineering roles inside large companies.
In this learning path, we mention the most important skills you need to land data engineering jobs in large technology companies. This learning path is more toward data engineers who work with the analytics teams rather than data engineers who work more with infrastructure teams.
Learn SQL
Learn Python
Learn Airflow
Learn Fundamentals of Warehousing Systems
Learn Hadoop, Spark & Hive
altLearners 