LinkedIn Content Strategy & Writing Style
AI & Data Platform Engineer | GCP & Fabric Certified
1 person tracking this creator on Viral Brain
Marcel Dybalski positions himself as a pragmatic architect of order in an industry often blinded by technical hype. His content strategy centers on the structural foundations of data leadership, using sharp sarcasm and frameworks like R.I.C.E. to expose the "dumpster fires" caused by poor governance and misaligned operating models. He is notable for his ability to bridge the gap between engineering and the C-suite, consistently translating complex concepts like Data Mesh or lakehouses into tangible business outcomes. By intersecting technical platform engineering with organizational psychology, Marcel differentiates himself as a strategist who prioritizes fixing "automated chaos" over simply deploying the next shiny AI tool.
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When I was a junior data analyst, I asked endless questions. "Why doesn't this join work?" "What's the difference between WHERE and HAVING?" "Can you look at my query... again?" Some seniors would'v…
At first glance, your data pipeline is flawless. The breakage starts where nobody’s looking. Dashboards are green. SLAs are being met. Jobs are “successful”. And still the business is making decisi…
There is one word that destroys data teams. They use it constantly and wonder why nothing ships. "Yes" Here's the framework I use to stop bad projects before they drain momentum: R.I.C.E Score = (…
Your CEO doesn't care about your data platform. Not even a little bit. And that's YOUR fault, not theirs. You talk in architectures. They think in outcomes. Then they wonder why the budget got cut…
Let's clear this up once and for all. Data Architecture ≠ Data Infrastructure. But most teams treat them like they're the same thing. That's the problem. They're not. Architecture = Your blueprint…
You hired 15 data people. Why does nothing ship faster? Because structure matters more than headcount. Most leaders think scaling means adding bodies. But the wrong model turns growth into gridlock…
8.8 posts/week
Posts / Week
1 days
Days Between Posts
2
Total Posts Analyzed
HIGH
Posting Frequency
653.8%
Avg Engagement Rate
STABLE
Performance Trend
230
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.78/10
Uniqueness Score
YES
Question Usage
0.3%
Response Rate
Writing style breakdown
<start of post>
How to ensure your AI strategy fails by 2026
Step 1: Focus entirely on the LLM
↳ Who cares about the data? The model is "smart" enough to fix it.
Step 2: Ignore the "boring" stuff
↳ Data contracts? Governance? Lineage? That's for people who don't like innovation.
Step 3: Build in a vacuum
↳ Don't talk to the business units. Just build a "cool" chatbot and hope they find a use for it.
Step 4: Treat data quality as a "later" problem
↳ "We'll clean the lakehouse once the AI is running." (Spoiler: You won't).
The result?
A very expensive hallucination engine.
The business doesn't want AI.
They want answers they can trust.
But trust isn't a feature of the model.
It's a property of the foundation.
You can't build a skyscraper on a swamp.
And you can't build reliable AI on a "data mess."
Stop buying the hype.
Start building the core.
➤ Follow Marcel for insights on data that drives decisions, not decoration.
🔔 Tap the bell on my profile to get notified when I post.
♻️ Repost to help a leader avoid the AI money pit.
What's the biggest AI red flag you've seen?
<end of post>
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