CASE STUDY 06
- Professional
- Data Engineering
- BI
From manual tracking to automated reporting
An ELT pipeline deployed to production, scoped with management.
JobAppeal · Freelance
The project, step by step
ELT in productionGmail API → BigQuery → Power BI
- 01
Extract communications
Gmail API
- 02
Load the data
Storage in BigQuery
- 03
Present the metrics
Reporting in Power BI Service
Cloud Scheduler orchestrates pipeline execution.
Context
Freelance Data Analyst / BI assignment in Montpellier from October 2025 to March 2026, scoped directly with management.
Problem
Replace manual tracking of customer communications with an automated process.
Data
Customer communications extracted through the Gmail API.
Approach
I deployed an ELT pipeline to production to support reporting in Power BI Service.
Method
Extraction through the Gmail API, loading into BigQuery, orchestration with Cloud Scheduler and reporting in Power BI Service.
Results
The pipeline was deployed to production and replaced manual tracking with an automated process.
Technologies
Gmail API, BigQuery, Cloud Scheduler and Power BI Service.
Lessons and limitations
Data volume, execution frequency, user count and time saved remain to be documented. No quantified improvement is attributed to this assignment.