AutomationDelivered

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

RFQ Intake

Rotabull feed

Dagster ETL

Scheduled + monitored

Flask API

Sheet updates

Apps Script

User-triggered sync

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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