Hey folks! 👋 I've been piloting Windsurf with my data team for the last two months, specifically focusing on how to bake its code suggestions directly into our pull request process. The goal was to cut down review cycles and catch potential issues *before* they hit the main branch. I'm really happy with the workflow we've landed on—it feels like a genuine force multiplier.
Here's the step-by-step we follow now:
* **Pre-PR: Local "Windsurf Pass"** – Before anyone opens a PR, they run a quick local review. We use the Windsurf extension in VS Code to generate explanations for complex logic and ask it to spot anti-patterns (like N+1 queries in our data pipelines or inefficient aggregations). This is a great self-check.
* **PR Description Template** – We added a new section to our PR template: "Windsurf Insights." The author pastes in any key suggestions or explanations Windsurf provided about the changes. This gives reviewers immediate context and shows proactive thinking.
* **Reviewer Sidecar** – During review, we keep Windsurf open in a side window. For any tricky SQL transformations or dashboard calculation logic (we use Looker and Metabase), we'll pop the relevant block into Windsurf to double-check the suggestion or get an alternative take. It's fantastic for spotting edge cases in `CASE` statements or JOIN conditions.
The biggest win has been on cost optimization. Windsurf is surprisingly good at flagging overly broad date filters in warehouse queries or suggesting more efficient table functions. It's caught a few things that would have silently bloated our BigQuery bills. 🎯
We did have to set a couple ground rules: its suggestions are a starting point, not a mandate, and the human reviewer still owns the final approval. But it's made our discussions much more focused and educational.
Has anyone else tried something similar? I'm curious how you're handling the balance between automated suggestions and human judgment in your review gates.
Data doesn't lie, but dashboards sometimes do.
Your "Pre-PR: Local 'Windsurf Pass'" is a solid starting discipline, but it's voluntary. That's the weak link.
You need to make that check mandatory and structured, otherwise under deadline pressure engineers will skip it. In my team, we solved this by adding a pre-commit hook that runs a script. The script doesn't use Windsurf directly, but it uses the same underlying patterns to flag specific issues we've codified, like inefficient DataFrame joins in Spark or missing watermark keys in our stream jobs. It fails the commit if those are present, forcing a fix *before* the PR even gets drafted.
Relying on pasting insights manually into the PR description also creates drift. We found that within a month, engineers started just writing "No insights" in that section to skip the step. Consider automating that annotation via a lightweight CI job that comments on the PR diff instead, so the context is attached to the specific lines of code.