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monitoring: "Your comp vs the market" panel on Job Hunter dashboard

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Add a barchart (panel 10) ranking every company's London p50 total comp
(COALESCE total/base) with the user's current comp shown in line, so it's a
direct "how do I compare" view. The user's figure is NOT hardcoded in the
dashboard JSON — it's a labeled comp_point in the DB (company_slug
'self-current', source 'self', "Me (Meta IC5)"), keeping the sensitive number
out of git. It's below the £500k alert bar (no Slack ping) and ranks too low
to appear in analyze leaders. Runbook documents the panel + how to update the
baseline.

[ci skip] — dashboard ConfigMap applied locally (targeted).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Viktor Barzin 2026-06-02 21:23:01 +00:00
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docs/runbooks/job-hunter.md
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@ -255,6 +255,37 @@ WHERE c.slug = 'janestreet' AND s.snapshot_date IN ('2026-06-02', '2026-08-30')
GROUP BY c.display_name, s.snapshot_date;
```
### "Your comp vs the market" dashboard panel + your baseline
The Job Hunter Grafana dashboard (`grafana.viktorbarzin.me` → Job Hunter) has a
bar chart **"Your comp vs the market — London p50 total comp"** ranking every
company's London median TC with your current comp shown in line. Your figure is
deliberately **not hardcoded in the committed dashboard JSON** — it lives in the
DB as a labeled comp_point (`company_slug='self-current'`, `source='self'`,
display "Me (Meta IC5)"). It sits below the £500k alert bar (never pings Slack)
and ranks too low to surface in `analyze` leaders — it only appears on this
chart and as its own `comp-table` row.
Update it when your comp changes (the only place the number lives):
```bash
kubectl -n job-hunter exec deploy/job-hunter -- python -c "
import asyncio; from decimal import Decimal; from datetime import date
from job_hunter.db import create_engine_from_env, make_session_factory
from job_hunter.sources.comp.base import CompPoint
from job_hunter.storage_comp import upsert_comp_point
async def m():
e=create_engine_from_env(); sf=make_session_factory(e)
async with sf() as s:
await upsert_comp_point(s, CompPoint(source='self', external_id='self-current',
company_slug='self-current', company_display_name='Me (Meta IC5)',
level_slug='senior', location_bucket='london',
total_value=Decimal('267000'), currency='GBP', effective_date=date.today()))
await s.commit()
await e.dispose()
asyncio.run(m())"
```
### Interpreting the numbers — caveats
- **Sample size**: `analyze` flags companies with `n < 3` as `low_confidence`. A single self-reported datapoint is anecdote, not a band — chase the p50 only where n is healthy.