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5 Data Engineering Mistakes That’ll Haunt You (and How to Avoid Them)
Manage episode 495669810 series 3678766
Is your team spending time repairing broken data pipelines?
In this live session, I’ll walk you through 5 of the most common—and costly—mistakes I’ve seen in 25+ years of data engineering. From schema drift disasters to overengineered tech stacks, I’ll break down real-world examples and teach you how to build smarter, cleaner, and more reliable data systems.
✅ What you’ll learn:
How to future-proof against schema changes
Why simple tools often outperform complex ones
Where data quality goes to die—and how to fix it
How to avoid secrets and paths hardcoded in shame
What maintainable pipelines actually look like
Whether you’re a junior engineer or a team lead, these lessons will save you time, money, and frustration.
💬 Have a mistake to share? Drop it in the chat... I’ll respond personally!
🔗 Need help fixing your stack?
Reach out: https://www.gambilldataengineering.com/consultation-request
#DataEngineering #ETL #DataPipelines #SQL #CloudData #Snowflake #Databricks #DataOps
Chris Gambill is a data engineering consultant and educator with 25+ years of experience helping organizations modernize their data stacks. As founder of Gambill Data, he specializes in data strategy, cloud migration, and building resilient analytics platforms for mid-market and enterprise clients. He’s passionate about making real-world data engineering accessible.
Connect with Chris on LinkedIn or learn more at gambilldata.com.
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