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Discover key lessons from 15 years in data engineering. Explore do’s, don’ts, and best practices for 2025, from data lineage and contracts to observability and AI readiness.
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Fifteen years ago, when I wrote my first ETL pipeline, I thought data engineering was all about moving data from Point A to Point B. Clean it, transform it, and load it into a warehouse—that was the job.
Fast forward to 2025, and the role of a data engineer is far more complex—and far more critical. We are no longer just building pipelines; we are laying the foundations for AI, analytics, and enterprise decision-making. We don’t just “move data”; we build trust in data.
Over these years, I’ve made mistakes, seen failures, and learned painful lessons. I’ve also witnessed what works—the practices that scale, sustain, and set teams up for long-term success.
This article is my attempt to distill those lessons for anyone navigating data engineering in 2025—whether you’re just starting or leading teams.
Three major shifts have redefined our profession:
With that in mind, let’s break down the Do’s and Don’ts of modern data engineering—lessons I wish I had internalized sooner.
Don’t wait until an auditor, regulator, or frustrated data scientist asks, “Where did this column come from?” Build lineage tracking into your pipelines.
In my experience, lineage isn’t just compliance—it’s productivity. It reduces “debugging by Slack thread” and accelerates onboarding.
A lesson I learned the hard way: if producers and consumers don’t agree on schemas, chaos follows.
Data contracts create accountability, and in 2025, they’re as critical as unit tests in software.
Monitoring tells you something broke. Observability helps you understand why.
Think of observability as “CCTV for your pipelines.”
Metadata is no longer optional documentation; it is the foundation for AI-ready data.
If data is fuel, metadata is the GPS—it tells you where it came from, where it’s going, and why.
In data engineering, perfectionism is a trap.
Failing fast saves you from sinking months into pipelines no one actually uses.
In 2025, privacy regulations (GDPR, CCPA, PDPA, etc.) are expanding, and AI governance is a boardroom topic.
Don’t bolt on compliance later—it’s always costlier.
With cloud bills spiraling, cost-awareness is part of the job.
I’ve seen companies save millions by simply auditing stale datasets nobody used in years.
Documentation isn’t writing essays—it’s making future engineers (or yourself) productive.
Good documentation is like good lineage: invisible when it works, painful when it’s absent.
Data engineering success isn’t “99% pipeline uptime.” It’s whether the business made better decisions.
This shift has been key for me in conversations with executives and investors.
The only certainty is that tools and requirements will change.
Resilience isn’t building an unbreakable system—it’s building one that can evolve.
Every year, a new “game-changer” emerges. In 2025, it might be a flashy AI-native data orchestrator.
Don’t rip apart your stack just to chase trends. Tools are enablers, not saviors. Focus on fundamentals: modeling, contracts, and governance.
“Garbage in, garbage out” is cliché, but still true.
Don’t wait until leadership asks, “Why is revenue off by 20%?” Embed data quality checks upfront—null checks, reconciliation, anomaly detection.
The longer you wait, the harder it is to fix.
Too many teams see governance as red tape. In reality, governance is context: ownership, definitions, and access.
Avoid the trap of over-engineering policies that nobody follows. Instead, make governance part of daily workflows.
Every engineer wants to build elegant, multi-layered architectures. The problem? Complexity multiplies failure points.
If your pipeline requires five systems and three handoffs, you’ve likely overdesigned it.
Keep it simple, especially in the early stages.
I once built a beautiful set of pipelines only to find out the business didn’t need them.
Don’t assume requirements—validate them. Align with product managers, analysts, and business leaders before writing code.
Pipelines don’t fail in isolation—teams fail.
Culture matters as much as technology.
In 2025, many executives assume, “We’ll just throw an LLM at it.”
Don’t fall into that trap. AI depends on structured, trusted data. If your lineage is broken, AI will hallucinate confidently—and dangerously.
Manual fixes scale poorly.
Automation isn’t optional—it’s survival.
Your best query optimization won’t matter if you can’t explain its impact to leadership.
As engineers, we often think in code, but leadership thinks in outcomes. Bridge that gap.
It’s easy to obsess over pipelines and miss the fact that the end goal is enabling decision-making and innovation.
Don’t get stuck optimizing the wrong thing. Step back and ask: Why does this data matter?
If I had to summarize where data engineering is heading:
And this is where platforms like Decube (yes, I’m biased) are playing a huge role. By unifying data observability, contracts, catalog, and governance, they help enterprises not just manage data, but trust it—laying the foundation for AI readiness.
After 15 years in the trenches, my biggest lesson is this: data engineering is less about moving data and more about creating trust.
Trust that numbers are accurate. Trust that models aren’t biased. Trust that compliance won’t break. Trust that teams can move fast without chaos.
The tools will change. The buzzwords will change. But if you focus on trust—through lineage, contracts, observability, and context—you’ll always be ahead.
First step to AI Readiness
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Sneak peek from the data world.
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