How I Built an AI-Powered “On-Call Engineer” That Likely Knows Why My Pipeline Broke
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A data engineer built an automated Root Cause Analysis (RCA) engine that diagnoses dbt pipeline failures using Snowflake Cortex AI. The system works in five steps: dbt tests store failing rows via store_failures, a classifier routes failures by type (fanout, business rule, accepted values, data quality), evidence is collected including compiled SQL from a model registry, an LLM (Claude) generates adaptive diagnoses based on failure type and recent code changes, and results are posted to Slack via Snowflake's webhook notification integration. Key lessons include proper SQL escaping when embedding model code, per-model MERGE statements for isolation, encoding domain heuristics in prompts, and rate-limiting alerts to avoid noise.
