13 entries tagged "data engineering" — 9 posts, 4 links.
A senior data engineer's decade of candid career lessons — job mobility, technology stacks, management dynamics, and data-engineering-specific realities — delivered in a deliberately irreverent tone.
The value isn't novelty; it's calibration. The framing strips out the polish that makes most career advice easy to agree with and harder to actually use.
This is worth keeping because bronze, silver, and gold layers remain a common language for data platform design. Even when the exact stack changes, the idea of progressive refinement is still useful.
The caveat is that medallion architecture is not a substitute for ownership or quality rules. A named layer does not make data trustworthy by itself.
This is a small but useful data-team link because SQL style is a collaboration tool. A query that is easy to review, modify, and debug is a data product maintenance win.
The guide should not become dogma. Its value is giving teams a baseline so code review can focus on logic instead of formatting taste.
This is useful because data modeling is still one of the quiet foundations of analytics, ML, and application design. Good models make downstream work boring in the best way.
The link is older in spirit than the AI material, but that is exactly why it belongs here. New tools keep inheriting old modeling mistakes.