I spent years in engineering engineering for companies, from high growth companies to global giants. But a moment made me question everything: when three different reports on the same metric landed on my desk with three different answers. We had modern tools. We had smart people. What we didn't have was data confidence.
This experience led me to revise our whole database, not to continue better tools, but to create something much more difficult: confidence.
We had all the usual suspects, snowflake, databricks, dbt. But behind the scenes? Thug scripts. Abandoned dashboards. Hidden dependencies. It was disguised chaos.
What I did was the following: the problem was not what we used. It was what we couldn't see. The battery worked, but the pipeline did not do it. It had become a tangled waste of shadow workflows that no one fully understood.
This is a widespread problem. According to a 2023
I knew we had to reset.
From pipeline manufacturer to liability architect
My job title indicated “data engineer”, but my real work has become the responsibility in the system. This meant building railing, not doors:
- Define clear data contracts between teams
- Creation of a surveillance layer for the drift of the scheme and the failure of employment
- Design interfaces that have made the data reliable, not just accessible
We have not slowed down the analysts. We gave them systems on which they could count. We have adopted data quality tools that could point out almost real and invested anomalies in diagram registers to lock the way the data has been shared between the trade areas.
Why federated governance worked for us
Centralization failed us. Too slow, too opaque. But chaos was not the answer either. What worked was federated governance.
We let domain teams own their pipelines, but according to shared standards:
- A unified metadata catalog
- Access controls based on tags
- Use monitoring to report dead or poorly used data sets
We have modeled our approach after the principles exposed in the Zhamak Dehghani
Observability has changed the game
If you can't see it, you can't trust him. This is the reality I have experienced. We therefore made non -negotiable observability.
We presented:
- Real -time alerts on pipeline health
- End -to -end line so that each metric can be traced
- Analysis at the request to identify ineffective models
Tools like Monte Carlo and Opening have helped, but it was the cultural change that counted the most. We have not only recorded events, we made them significant.
We have also built dashboards to follow the freshness of the data and the rate of anomalies, making reliability a KPI, not a reflection afterwards.
What I would say to any business data manager
You don't need more tools. You need more visibility. You don't need more strict control. You need better collaboration.
If you swim in dashboards but you drown doubt, it's time to step back. Ask difficult questions. Rebuild if necessary. The king will not come from faster queries, she will come from better decisions.
Your goal is not perfect data. These are reliable, explainable and reliable data. This is what cares about business leaders. And if you are a data leader reading this, I leave you one last thought: when you start to treat your pipelines like products, everything changes.
Trust me. I did it once. And I would do it again.
