Build a US Jobs Dashboard with BLS Data

A useful labor dashboard needs more than one headline. The unemployment rate describes people in the labor force, payroll employment counts jobs reported by establishments, and earnings series add a compensation view. The BLS Labor Statistics Scraper lets one Task retrieve those official series in a consistent row format.

Preserve the survey boundaries

Start with a small, explainable set:

{"seriesIds":["LNS14000000","CES0000000001","CES0500000003"],"startYear":2024,"endYear":2026,"maxResultsPerSeries":36}

Do not join observations only on a formatted month label. Use year plus BLS period, and retain the series ID as the metric key. The unemployment series is a rate, payroll employment is a level, and hourly earnings use currency units; put each on an appropriately labeled axis rather than normalizing them without explanation.

For release-day automation, schedule the Task after BLS publication, write results to a revision-aware table, and compare the current snapshot with the last successful one. Alert on missing expected periods as well as changed values. A successful HTTP response with no observations should not overwrite yesterday's dashboard.

Footnotes and the latest flag belong in the warehouse even if they do not appear on the public chart. They help analysts distinguish preliminary or revised values during review. If the dashboard adds industry detail later, use separate series groups and data-quality checks for each release family. That keeps a fast-growing catalog of metrics from turning into an undocumented collection of numbers that happen to share a date.