Operational tooling and monitoring

The monitoring application that let the team actually see what the lead-sourcing pipeline was doing: which jobs were running, which had failed, and what a new processing cycle needed before someone could start one.

I moved this app's own deployment onto Lambda partway through its life, added ETL-specific monitoring so pipeline health was visible without someone tailing logs, and built out an Athena-oriented dashboard summarizing recent processing jobs and their status. Slack-integrated reporting pipelines pushed operational alerts and summaries out automatically, so the team found out about a stalled job from a notification, not from a customer noticing stale data first.

This project taught me something that carried into how I think about AI/data pipelines generally: the pipeline itself is only half the job, the other half is building something that tells you honestly when it's not working.