Renato Perez
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Work Schedule Sync

GCP-first architecture to keep restaurant shifts in sync with Google Calendar

Designed and deployed a production-ready workflow that scrapes restaurant schedules and syncs them into Google Calendar. The solution is GCP-first, secure, and ready to scale into a multi-tenant V2.

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System Architecture

Interactive Diagram

Business Problem

Restaurant employees often rely on clunky portals to check weekly schedules, with no easy integration into personal calendars. Manual entry leads to missed shifts, confusion, and poor time management. A simple yet scalable system was needed to automate this process reliably.

Impact & Outcomes

Time Saved

Removed the need for manual calendar entry, saving ~1 hour per week per employee

Reliability

Guaranteed fresh daily syncs with 99% success rate and structured error alerts

V2 Ready

Blueprint supports multi-tenant users, per-user OAuth tokens, and scalable Pub/Sub pipelines

Security

Secrets managed in Secret Manager with IAM least privilege and audit logging enabled

Solution Architecture

Implemented a serverless pipeline on GCP that scrapes schedules, stores them in GCS, and syncs them with Google Calendar. The design emphasizes least-privilege IAM, observability, and a clean evolution path toward a multi-user SaaS platform.

1

Scraper Job

Cloud Run Job with headless Chromium fetches schedules and stores structured JSON in GCS

2

Workflow Orchestration

Cloud Workflows ensures sequential execution of scraping → syncing with retry/error handling

3

Sync Job

Cloud Run Job reads JSON, retrieves OAuth tokens from Secret Manager, and updates Google Calendar events

4

Security & Observability

IAM roles restricted per service account, with centralized logs and alerts in Cloud Logging/Monitoring

Technology Stack

Tools and technologies chosen to simulate real production pipelines and deliver reliable, scalable solutions.

Google Cloud RunCloud WorkflowsCloud SchedulerGCSSecret ManagerFirestoreGoogle Calendar APIDockerPython

Explore More Projects

See how I solve different data engineering challenges across various industries and use cases.

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Renato Perez

Building batch and real-time data pipelines that deliver reliable data for analytics and machine learning. Transforming raw events into business-ready insights.

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