Databricks + Cursor · Spark job triage
When a stakeholder pings your data-platform channel about a slow ETL, you normally context-switch across Workflows, Spark UI, cluster events, and Unity Catalog before you even know where to look. Cursor reads all of it for you, runs five specialist agents in parallel to find skew, code, and cluster issues at the same time, and opens a reviewable PR with the evidence attached.
You stay in the review loop. The agent stays in the tools. Every skill, MCP, and prompt is open in .cursor/skills/ so your team can audit and adapt.
Catch the jobs your alerts miss
Your alerting policy pages on hard failure and schedule slip. Cursor catches the quieter problem: jobs running 2-5× over baseline that nobody knows about until a downstream dashboard breaks. Nightly sweep across your workspace, on-demand via Slack.
Recover compute you are paying for
A single ETL job that runs 4× longer than it should is roughly $1,500-2,000 of wasted compute per run, every run, forever. Cursor finds it, explains why, and ships the fix in one PR. Your DBU spend goes down, not your headcount.
You still own merge
Cursor never self-merges and never edits production from inside the agent. The output is a PR your on-call reviews, with a Codex behavioral-equivalence check, a 1% sample run, and the Spark UI evidence the agent used. You stay in the loop you already trust.
Guardrails every triage run clears