Member-only story
5 Mistakes New Data Engineers Make
Warnings to my past self
When it comes to best practices and business alignment, most new data engineers learn as they go.
From building overly complex and unsustainable systems to putting too much faith in existing data structures, here are five of the most common mistakes and traps that even the most skilled and talented new data engineers can fall into. I’ve also included what you can do to avoid the same pitfalls.
Common Mistakes That Tend To Trip Up New Data Engineers
By their very nature, massive datasets are imprecise, and it’s really easy for data engineers to lose sight of the forest for the trees. A common theme among new data engineers is highly technical systems that are difficult to maintain in the long run and don’t keep the end-user and overall business objectives in mind.
1. Building unmaintainable systems
Many new data engineers build programs that may work just fine and deliver a specific end result in the short term, but they fall apart or are too complicated to maintain in the long run. ETL systems and data warehouses that are too reliant on complex code and can’t be managed without the original data engineer’s input are unsustainable and ultimately inefficient.
