Sam Miraki’s Post

Clean and motivating: 🌅 Good Morning! Start your day with a data mindset. Every successful data pipeline starts with one thing: 👉 Clear thinking before coding Before writing a single line, ask: 💡 What problem am I solving? 💡 What does the business actually need? 💡 Is my data reliable? 🚀 Great data engineers don’t just build pipelines; they build solutions that matter. Make today count. One clean pipeline. One solid improvement. What’s your focus today? 👇 #DataEngineering #MorningMotivation #BigData #ETL #CareerGrowth #TechLife

🎯 𝗢𝗻𝗲 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝘁𝗵𝗮𝘁 𝗺𝗮𝗱𝗲 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗰𝗹𝗶𝗰𝗸 𝗳𝗼𝗿 𝗺𝗲 ... 🎯 👉 𝗠𝗲𝗱𝗮𝗹𝗹𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Before this, I used to: ❌ Clean data randomly ❌ Build directly on raw tables Now I think in layers: 🥉 𝗥𝗮𝘄 → untouched 🥈 𝗖𝗹𝗲𝗮𝗻 → structured 🥇 𝗚𝗼𝗹𝗱 → business-ready Simple idea. Huge clarity. 👉 𝘋𝘢𝘵𝘢 𝘪𝘴 𝘯𝘰𝘵 𝘤𝘭𝘦𝘢𝘯𝘦𝘥 𝘰𝘯𝘤𝘦… 𝘪𝘵’𝘴 𝘳𝘦𝘧𝘪𝘯𝘦𝘥 𝘴𝘵𝘦𝘱 𝘣𝘺 𝘴𝘵𝘦𝘱. If you're learning data engineering, don’t skip this. It will save you hours of confusion. #DataEngineering #DataArchitecture #LearningInPublic

🚀 What Breaks First When Data Scales? It’s usually not the infrastructure. It’s the assumptions. Assumptions like: • “This dataset won’t grow much” • “This schema will stay stable” • “This job will always run within time” • “This pipeline has only one consumer” At small scale, these assumptions hold. At large scale, they fail — and systems start to break. That’s why strong data engineering is built on: ✔ Designing for growth from day one ✔ Expecting schema evolution ✔ Planning for multiple downstream consumers ✔ Building flexible and scalable architectures Because scaling doesn’t just increase volume. It exposes every hidden assumption in your system. #DataEngineering #BigData #DataArchitecture #CloudData #Engineering

Do you actually need to take a pay cut to break into data engineering? Not always, if you position your skills right you can level up without stepping back.

Data pipelines don’t fail loudly they fail silently One of the biggest risks in data engineering isn’t crashes. It’s this: everything looks like it’s working Pipelines run. Dashboards load. Reports go out. But underneath: • data is delayed • fields are partially populated • logic changed without anyone noticing And suddenly: decisions are based on wrong data Strong data systems don’t just move data they validate it. That means: ✔ data quality checks at every stage ✔ monitoring for anomalies, not just failures ✔ clear ownership when things break Because “no errors” doesn’t mean “no problems” #DataEngineering #DataQuality #Analytics #Backend #DataOps

I used to think data pipelines were simple. Just move data from A to B… right? After 4 months of applying to data engineering roles, hundreds of applications, rejections, silence, and a few interviews, I started to realize something didn’t add up. Every company I spoke to had different problems. Different stacks. Different expectations. And the more we talked, the more I questioned: Why are their approaches so different? Why does the same “data pipeline” look nothing alike across companies? So I tried to simplify it in my head, like delivering fish from a lake to a chef. Still didn’t explain the complexity. Then it clicked. What if it’s not about moving data… but about navigating terrain? What if every company is climbing a different mountain— with different paths, different constraints, and different tools to get there? That’s when everything started to make sense. Maybe the goal isn’t to build one perfect pipeline. Maybe it’s to understand the terrain well enough to choose the right way up. 👉 Swipe to see how this analogy changed the way I understand data engineering.

A data engineer's true craft isn't just moving data, but sculpting the resilient infrastructure that turns raw information into organizational wisdom. #DataEngineering

A data engineer's true craft isn't just moving data, but sculpting the resilient infrastructure that turns raw information into organizational wisdom. #DataEngineering

One mistake I made early in my data engineering work was trying to make everything perfect before anyone used it. Perfect models, perfect naming, perfect structure… and still no one was actually using the data. At some point it clicked: a perfect model that nobody uses has zero value. A good enough model that supports a real decision is what actually matters. Now I think less about perfection and more about usefulness. Data systems get better with usage, not in isolation. Have you ever over-engineered something that ended up unused? #DataEngineering #DataModeling #AnalyticsEngineering

You only realize a pipeline was poorly designed when it starts to struggle. Not when it runs. But when data grows, small issues become real problems. That’s when you see the difference. Scaling data systems is not just technical. It’s a thinking problem. Senior data engineers don’t rely on different tools. They think differently. They design for failure, think in systems, and prioritize quality, cost, and observability from day one. I wrote about these principles in detail: 👉 7 Things Senior Data Engineers Do Differently When Designing for Scale Link in comments 👇 #DataEngineering #BigData #SystemDesign #DataPipeline #ScalableSystems