Reverse ETL Solved a Problem We Accidentally Made Up

We built a genuinely well-engineered reverse ETL pipeline for a client, syncing computed customer segments from their warehouse back into their email platform and ad audiences on a reliable schedule. It worked. It also took us about three weeks longer than planned, mostly spent reconciling customer identity across four source systems that each had a slightly different idea of who a given customer was.

About halfway through that reconciliation work, it became obvious that the actual problem wasn't 'we need reverse ETL,' it was 'we have four systems disagreeing about customer identity and no canonical source of truth.' Reverse ETL was the tool we reached for because it's the trendy answer to 'the warehouse has good data and the tools don't,' but in this case the warehouse didn't actually have clean data either — it just had a central place where the mess from four systems collided, which isn't the same thing as having solved the mess.

What reverse ETL is actually good at

When the warehouse genuinely does have clean, well-modeled data and the problem is purely about getting that data into operational tools on a schedule, reverse ETL is a great fit and I'll recommend it without hesitation. The distinction that matters is whether you're syncing clean data outward, or using the sync pipeline as a place to quietly patch over identity resolution problems that never actually got solved.

Reverse ETL moves data well. It doesn't clean data, and treating a sync pipeline as a substitute for a real identity resolution project just moves the mess somewhere harder to see.

What we actually did

• Paused the reverse ETL build and did a dedicated identity resolution pass first — establishing one canonical customer ID the warehouse models would key off of.
• Only after that resolution layer existed did the reverse ETL sync become the straightforward, low-drama pipeline it should have been from the start.
• Documented the identity resolution logic as its own standalone asset, reusable for the next three integration projects that came up afterward.
• Pushed back internally on treating 'warehouse-native' as synonymous with 'clean' — a warehouse can faithfully store four systems' worth of disagreement just as easily as it stores a single source of truth.