My 10 Biggest Fails as Data Engineer

These aren’t success stories — they’re the fails that taught me the most.

Hey friends, Happy Tuesday!

I’ve spent the last 15 years working on more than 15 different data projects. And here’s the part nobody really talks about… the biggest lessons didn’t come from the wins. They came from the mistakes.

So today I want to flip the script. Instead of celebrating wins, I’m going to share my 10 biggest fails in data engineering. The real ones from real projects. And more importantly, the lessons I learned so you don’t have to repeat them.

Grab your coffee. Let’s dive in.

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Fail #1: Schema Changes — “Looks Small, Hits Big”

This one still makes me cringe when I think about it.

I once made a schema change in the gold layer, thinking it was no big deal. Just a renamed column here, a type change there. Seemed harmless.

Within hours, everything started falling like dominoes. Dashboards showed errors. Reports wouldn’t load. Analysts and business users were messaging me in panic because their numbers had disappeared.

That’s when it hit me: the reporting layer is sacred. Even the smallest schema change can ripple through dozens of systems.

Lesson: never rename or drop things in the reporting layer. Evolve instead of breaking. Add new columns, version your tables, and give people time to adjust

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Fail #2: Loading Everything — “Data Hoarder”

When I got a new requirement, my first thought was always technical: new source system = load it all into the lake, who knows we might need all!

So one time, without asking many questions, I connected to the source and started soaking up everything. I ended up loading more than 150 tables into the lake.

Fast forward a few months. I checked the logs. Only 3 of those tables were ever used. When I finally asked the business, they said, “Yeah, those 3 were all we needed from the beginning.”

That’s when it hit me. I hadn’t just wasted time. I wasted resources too — cloud storage, compute, cluster costs. All because I didn’t start with the simple question: what do you actually need?

Lessons: don’t do bottom-up data engineering. Don’t load blindly and hope it’s useful later. Start top-down. Ask the business what they really need, then bring in only the data that matters. A bigger lake doesn’t mean more value. A smaller, focused lake built on actual needs does.

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Fail #3: Tailoring Everything in Gold — “The Report Factory”

When I first started modeling data in the gold layer, I thought the best way to serve customers was to tailor everything. Each new report or customer got their own shiny table. Need an extra column? No problem, I’ll spin up a new version.

Fast forward a year. After 100+ reports and customers, I was drowning. I was writing the same queries again and again. The same data lived in multiple places, pipelines took forever to load, and storage costs went through the roof. Every small change turned into a nightmare to maintain.

That is when it hit me: the gold layer is not about tailoring for every single use case. It is about building a solid, generic model that multiple reports and customers can consume. Write it once, reuse it everywhere.

Lesson: resist the urge to build one-off tables for every request. Learn proper data modeling and design gold as a reusable layer. It will save you compute, storage, and a lot of sanity.

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Fail #4: Pipelines Green, Data Red — “The Silent Failures”

At first, I thought pipeline success was all that mattered. The job ran, logs showed green, no errors. Done.

But then reality hit. A “successful” run doesn’t always mean correct data. I once had a pipeline finish smoothly… but it skipped an entire day of data because the upstream file never arrived. Another time it loaded everything twice!

There were no red flags in the logs. Just silent failures in the data.

Lesson: job monitoring isn’t enough. You also need data monitoring. Track last update times, duplicates, unexpected nulls, and table growth. A green checkmark means nothing if the data is wrong.

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Fail #5: The Stubborn Architecture — “Five Years of Pain”

Well, this one wasn’t me. But I saw it up close, and it was painful to watch.

One engineering lead was convinced their warehouse design was perfect. No bronze, silver, gold separation — just a custom mix of “whatever works.” Business logic in raw tables, cleanups in gold, shortcuts everywhere.

The problem wasn’t just the design. The problem was stubbornness. They kept building on it, year after year. By the time migration came, we were stuck with five years of tech debt, messy pipelines, and a warehouse nobody fully understood.

Lesson: bad design is painful, but doubling down on bad design is worse. Don’t let ego drive architecture. Stick to clear standards and evolve when needed, otherwise you’re just locking in chaos for years.

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Fail #6: The Untouchable Pipeline — “Nobody Dares Change It”

In my first year as a data engineer, I once built a pipeline that was way too big, way too complex, and way too undocumented. At the time, I thought I was being clever. It handled everything in one flow, had all the edge cases baked in, and it worked. The problem? It only worked because I understood it.

Nobody else dared touch it. If something broke, the whole team would wait for me. If I was out, things just stalled. And when it failed in production, it turned into a crisis because I was the only one who could debug it.

For a while, I felt proud… like I was indispensable. But later I realized it was a failure of engineering. I hadn’t built a pipeline. I’d built a bottleneck.

Lesson: if only one person can understand or maintain a pipeline, it’s not a success, it’s a risk. Keep pipelines modular, document your logic, and design so anyone on the team can take over. A good engineer makes themselves replaceable, not irreplaceable.

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Fail #7: Ignoring Data Quality — “Shortcut That Backfired”

When I first started, I treated data engineering like a race. My job, I thought, was simple: build pipelines that move data to the lake as fast as possible. Everything else was a nice-to-have. Data quality checks? I told myself I’d add them later when I had spare time.

The shortcut worked… for a while. But then the complaints started. Numbers didn’t add up, dashboards showed strange results, and people stopped trusting the reports. Under the hood, we found duplicates, missing IDs, and corrupted records everywhere.

Lesson learned: pipelines without quality checks are ticking time bombs. You might get away with it for a few months, but when it blows up, the damage is trust — and trust is the hardest thing to rebuild. Data quality is not a luxury. It’s the foundation.

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Fail #8: Hardcoding Everything — “Worked in Dev, Exploded in Prod”

This one is a little embarrassing, but it has to be said.

In one of my first projects, I hardcoded everything — file paths, schema names, even cluster names. It worked fine in dev, so I didn’t think twice.

Then we hit production and everything broke. Different paths, new schema prefixes, new cluster names. Every job failed, and I spent nights fixing scripts by hand.

Lesson: build flexibility into your pipelines. Use config-driven design and parameters. Hardcoding feels faster at first, but it multiplies your maintenance pain later.

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Fail #9: Ignoring Documentation “Reverse Engineering Hell”

I once worked on a project where the lead believed “the code is the documentation.” At first, it seemed fine. Everything worked, so why waste time writing docs?

Fast forward a couple of years. New engineers joined, and every single change turned into detective work. People had to reverse-engineer transformations just to understand what was happening. Simple fixes took days because nothing was explained anywhere.

Lesson: code is not documentation. Write down the logic, the why, and the data flows. Future you, and anyone who joins your team, will thank you.

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Fail #10: INNER JOIN My Old Friend

This one is a small fail, but worth sharing.

When I first learned SQL, INNER JOIN was my hammer and every problem looked like a nail. It worked… until I noticed half my data was gone.

Lesson: INNER JOIN isn’t the default. Use LEFT JOIN unless you really want to filter.

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Those were 10 of my biggest mistakes in data engineering. Some cost time, some cost money, and a few cost trust.

The pattern is clear: shortcuts feel fast, but they create long-term pain. Good engineering is about discipline, asking the right questions, and building with standards from day one.

At first, it was hard to write this post. Nobody likes sharing their fails. But I realized this is way more important for you than just reading my wins and celebrations.

These 10 mistakes shaped me far more than my successes ever did. If they help you avoid even one of these traps, then it was worth writing.

Thanks for reading ❤️

Baraa

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New Video This Week

This week I released a new Python video all about how to Add, Remove & Update Lists.
We dive into append(), insert(), remove(), pop(), and updating values with indexing.

These operations are key in the real world because data is always changing—whether it’s adding new customers, cleaning out bad records, or updating product details.

Also, here are 3 complete roadmap videos if you're figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap

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Hey friends —

I’m Baraa. I’m an IT professional and YouTuber.

My mission is to share the knowledge I’ve gained over the years and to make working with data easier, fun, and accessible to everyone through courses that are free, simple, and easy!