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MUYou DO NOT need to learn everything to become a Data Engineer.
People often prepare for mid-level roles while applying for entry-level roles.
Here’s what actually mattered for me in the beginning when switching from testing to a data engineer role.
1. SQL(non-negotiable): You’ll need to know the basics and complexities of sql along including subqueries and window functions.
If you’re not strong in SQL, you won’t be able to move forward in interviews.
2. Python concepts basics like lists, dictionaries, sets and basic problem solving.
You can solve questions in other languages too but I’d suggest Python as it’s easy to learn.
You don’t need hardcore DSA for most entry-level Data Engineering roles, but DSA is definitely important.
3. Data warehousing concepts like facts vs dimension, star vs snowflake schema, SCD Type 1,2 etc.
Understanding concepts and what data warehousing is and why it’s there mattered more than tools.
4. ETL and data pipeline understanding. How data is extracted, transformed, loaded is the CORE of Data Engineering.
You don’t need spark understanding in the beginning, just the understanding of how data flows in and out.
5. System design basics, not like design twitter/uber. Simple understanding of how data moves end to end and overall understanding of data eco-systems.
No deep design is expected at entry-level.
6. Pick any one cloud. Don’t chase all clouds, just any one cloud and cover its basics because you’d most likely be working on some cloud in your work.
I moved from Testing to Data Engineering by focusing on these basics, instead of trying to learn every other tool out there, and it is still the very core of Data Engineering which one must know to crack interviews.
Save this if you’re planning to make a switch into Data Engineering.
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#dataengineer #dataengineering
@muskan.khannaa
