Julia Moser·
Designed an automated reporting pipeline that turns my 3-hour Monday into 10 minutes of review
Designs automated reporting workflows that pull data, format insights, and distribute reports on a schedule.
Automated Reporting Pipeline Designer
You are a data automation engineer. I need to automate this reporting workflow: {{report_description}}. Data sources: {{data_sources}}. Report frequency: {{report_frequency}}. Recipients: {{report_recipients}}. Current manual process: {{current_process_description}}. Available tools: {{automation_tools}}. My SQL/technical skill: {{technical_level}}. Design: (1) Data extraction layer (what to pull, from where, how often), (2) Transformation logic (calculations, filters, aggregations), (3) Visualization/formatting specifications, (4) Distribution method and schedule, (5) Error handling for missing data, (6) Version control and archive strategy, (7) Implementation phases from manual → semi-automated → fully automated. Provide SQL/queries if applicable, or no-code alternatives. Include a architecture diagram in text format.
Ergebnisse
Designed an automated pipeline for your weekly sales report. Currently you spend ~3h every Monday pulling it by hand — this gets it to ~10 min of review.
## Architecture (text)
```
[HubSpot CRM] ──┐
[Stripe] ──┼──▶ [Extract: scheduled SQL/API] ──▶ [Transform: Sheets/SQL]
[Google Sheets]─┘ │
[Format: Looker Studio dashboard]
│
[Distribute: auto-email Mon 8am → exec list]
```
## 1. Data extraction layer
- HubSpot deals (closed-won, pipeline) via API → BigQuery, nightly.
- Stripe revenue via scheduled export → BigQuery.
- Manual targets from a Sheet (the one human input).
## 2. Transformation logic
- New-bookings = sum(closed-won this week).
- Pipeline coverage = open pipeline / quarterly target.
- WoW deltas + 4-week trailing average.
- Filter: exclude internal test accounts (`email NOT LIKE %@ourco%`).
## 3. Visualization spec
Looker Studio: 4 KPI scorecards (bookings, pipeline, win-rate, ARR) + a 12-week trend line + rep leaderboard table.
## 4. Distribution
Scheduled email Monday 8:00am to the exec list, PDF snapshot + live link.
## 5. Error handling
If any source returns 0 rows or a null total → hold the send, alert you in Slack instead of emailing execs a broken report.
## 6. Version control + archive
Each Monday's PDF auto-saved to a dated Drive folder. Query definitions live in a git repo.
## 7. Implementation phases
1. **Manual+:** build the BigQuery views, run them by hand. (Week 1)
2. **Semi-auto:** Looker dashboard live; you eyeball + forward. (Week 2)
3. **Fully auto:** scheduled query + scheduled email + the null-guard. (Week 3)
No-code alt if BigQuery is too much: Zapier + Google Sheets pivot + scheduled Sheet email.
Modell: Claude Sonnet 4
18 Likes5 SavesScore: 13
1 Kommentar
Noah Steiner·
Saved. The decision matrix took the agonizing out of a hard call.