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Data teams
Pipelines that repair themselves, connectors that build themselves. A data team’s backlog is half plumbing: a new source to connect, a nightly job that broke, a migration nobody has time for. Agents do plumbing well — Altoquant gives them schedules, verification and a place where every run is kept.
You say itonce, in a terminal
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Live sessionan agent works, you watch
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Jobsscheduled, verified runs
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Kept locallyfiles + full history, yours
Session — codex · broken pipeline
~/data ❯ job night-etl failed 02:14 — remediation ran
● cause: supplier API now paginates at 100
● patched etl/suppliers.py → re-run
loading ████████████████ 12,409/12,409 ✓
feed quality, 7 days
rows ▇▇▆▇▇▇▇ ok
nulls ▁▁▁▂▁▁▁ 0.8%
fresh ✓ all < 25h
› good — add a weekly data-quality report on this feed
● jobs/dq-suppliers.yaml created (Mon 07:00)
The pipeline DAG
night-etl — 02:00
extract ──┬─ suppliers ✗→✓ repaired 02:14 — pagination
├─ orders ✓ 8,120 rows
└─ prices ✓ 1,440 rows
↓
transform ✓ 31s → load ✓ 12,409 rows → verify ✓ counts
quality 7d rows ▇▇▆▇▇▇▇ · nulls 0.8% · fresh ✓ all < 25h
The jobs that emerge
night-etldaily 02:00The nightly load, verified by row counts and schema checks; routine breaks repaired automatically.
source-watchdailyUpstream APIs and exports checked for schema or pagination changes before they break the night.
dq-reportweeklyFreshness, volumes and null rates per feed, summarized with the runs to prove it.
one-off-migrationson demandBackfills and system migrations run as babysat batch jobs with a log per record.
Where the data lives
Credentials stay in your keychain, referenced by name. The full output of every run is kept, so “what did the pipeline actually do last Tuesday” is a lookup, not an investigation.
What you get
- Nights that fix themselves; mornings that start with a summary, not logs
- New connectors built in a session, then scheduled like everything else
- Verification on every load — silent partial failures stop being possible
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