There's a cold war happening in most organizations, and nobody's talking about it.

On one side: Data Engineering.

They live in Snowflake, dbt, and Airflow. They care about data quality, lineage, and making sure the analytics team can actually trust what they're looking at. Their nightmare? Garbage data flowing downstream because someone in Sales changed a picklist value without telling anyone.

On the other side: RevOps.

They live in Salesforce, HubSpot, and a dozen integration tools. They care about pipeline velocity, forecast accuracy, and making sure reps can actually do their jobs. Their nightmare? Some well-meaning analyst breaks a critical workflow because they don't understand how the Lead object actually gets used.

These two teams should be best friends.

They're both obsessed with data integrity. They both spend way too much time fixing things that broke because of undocumented changes. They both get blamed when the numbers don't match.

Instead, they barely talk. They operate in parallel universes with different tools, different vocabularies, and different reporting structures. When something breaks — and something always inevitably breaks — they point fingers across the chasm.

The root cause isn't the people. It's visibility.

Or, the lack thereof.

Data Engineering can see what happens once data lands in the warehouse. They can trace lineage from raw tables to dashboards. But they're blind to what happens upstream in Salesforce — the validation rules, the automation, the business logic that determines what data even makes it to them.

RevOps can see their Salesforce environment (sort of). They know which fields matter for which processes. But they have no idea how downstream systems depend on their work. They can't see which dashboards break when they rename a field or which ML models depend on data flowing through a specific automation.

Both teams are operating with half the picture. And when you can only see half the picture, every change feels risky. So changes pile up. Technical debt accumulates. And the gap between "what Salesforce says" and "what the dashboard says" grows wider.

Metadata is the missing translation layer.

Here's what metadata actually provides: a shared understanding of what data exists, where it flows, how it's used, and what depends on what.

When Data Engineering can see that a Salesforce field feeds a Snowflake table that powers a board-level dashboard, they can flag it before someone makes a breaking change.

When RevOps can see that their "temporary" workflow has become load-bearing infrastructure for the data team, they can think twice before modifying it.

This isn't about giving one team control over the other. It's about giving both teams the context they need to make good decisions independently.

What the peace treaty actually looks like

It's not a weekly sync meeting (though those help). It's not a shared Slack channel (though that helps too). It's a shared source of truth about how your systems connect.

That means:

Your Salesforce metadata — objects, fields, automations, validation rules—needs to be visible alongside your warehouse metadata. Not in separate tools that require switching contexts. In one place where you can trace a field from its origin in Salesforce through its transformation in dbt to its final destination in a Tableau dashboard.

Your change management needs to span both worlds.

When RevOps proposes a Salesforce change, the data team should automatically see potential downstream impacts. When Data Engineering modifies a transformation, RevOps should know if it affects anything they care about.

Your documentation needs to be unified. Field descriptions shouldn't live in a Confluence page that nobody updates. They should be attached to the metadata itself, visible wherever that field appears across your stack.

The organizations that figure this out have a real advantage.

They ship changes faster because impact analysis takes minutes instead of days.

They break things less often because dependencies are visible, not hidden. They spend less time reconciling conflicting reports because everyone's working from the same underlying reality.

And critically, they're actually ready for AI. Because every AI use case—whether it's Agentforce in Salesforce or a custom model in Snowflake—depends on the AI understanding your data. Not generic data. Your data, with your business logic, your definitions, your relationships.

You can't explain your data to an AI if you can't explain it to yourself first.

The peace treaty starts with visibility.

Data Engineering and RevOps don't need to merge. They don't need the same tools or the same priorities. They don't even need to chat over lunch. They just need to see each other's world clearly enough to stop breaking each other's work.

Metadata makes that possible. Not metadata as an abstract concept, but metadata as operational infrastructure — connected, current, and accessible to everyone who needs it.

The cold war can end. But someone has to build the bridge.

Oh wait. Sweep already did.

Want to see how it all works and how everybody can play nice, everything can stay unbroken, and your business can benefit? Book a demo. We'd be happy to show you.