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RFQ Operations Automation — Joy Aviation

From a 5-hour manual backlog to sub-15-minute automated quotes

A two-phase automation for an aerospace-logistics supplier that replaced manual RFQ handling and rolling spreadsheets with Dagster orchestration plus a Flask and Google Sheets integration.

DagsterFlaskGoogle Sheets APIApps ScriptPythonPostgreSQL
Problem Statement

High volumes of RFQs arriving hourly from platforms like Rotabull created a backlog. Siloed pricing and sourcing teams caused quoting inconsistencies, with no orchestration, poor observability and Google Sheets limitations.

  • Manual RFQ processing created an hourly backlog.
  • Siloed teams produced inconsistent quotes.
  • No pipeline orchestration, monitoring or run-level observability.
  • Google Sheets lacked dynamic fetch and deduplication.
Headline Outcomes
< 15 min

RFQ processing time

~5 hours

Hourly

Sheet sync frequency

Weekly

80%

Time savings

The Solution

Phase 1 added Dagster ETL pipelines with scheduled jobs and visual monitoring. Phase 2 added a Flask REST API and Apps Script sidebar so business teams could trigger updates, with rolling sheets synced to the database hourly.

Dagster ETL with scheduled jobs and a visual run-monitoring UI.

Flask REST API updates Google Sheets; an Apps Script sidebar enables user-triggered refresh.

Rolling sheets synchronised with the database every hour.

Decoupled, config-driven architecture so jobs can fail and be re-run in isolation.

System Architecture

How the data flows

01

RFQ Intake

Rotabull feed

02

Dagster ETL

Scheduled + monitored

03

Flask API

Sheet updates

04

Apps Script

User-triggered sync

05

Rolling Sheets

Hourly DB sync

Result 01

Cut RFQ turnaround from a full day to a few hours with near-zero data errors.

Result 02

Gave business teams a custom UI without leaving their spreadsheet workflow.

Result 03

Left a foundation ready to extend with RBAC and a SaaS offering.

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Further reading

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