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TEAfter working as a data engineer, here are 5 things I wish I knew earlier:
1. It’s not just SQL or Python
Data engineering isn’t about syntax
It’s about moving data reliably between systems and transforming it correctly along the way
2. Testing data is surprisingly hard
Testing backend code is straightforward → input vs expected output
In data engineering → massive datasets, multiple columns, edge cases… validating correctness is a real challenge
3. It gets harder as you grow
Junior role → write SQL / PySpark pipelines.
Senior role → design architecture, ensure data governance, manage scalability, reliability, and costs.
4. “Pipelines once built are done” — wrong
Data pipelines break. Schemas change. Upstream systems fail.
Maintenance and monitoring are ongoing responsibilities, not one-time work.
5. “More tools = better engineer” — myth
Knowing 10 tools doesn’t matter.
Understanding fundamentals (data modeling, distributed systems, trade-offs) is what actually scales your career.
If you focus only on coding, you’ll plateau early.
If you understand data systems, you’ll grow fast.
💾 Save this for when the role starts feeling more complex than expected
💬 Comment if you’ve felt this shift already
🔁 Follow to keep your thinking sharp as you grow in data engineering
@techwithprateek
