Job Description: Data Engineer (Python + Google Cloud)

Role Overview
We are seeking a Data Engineer to design and implement a cost-efficient, scalable data pipeline that replaces a third-party automation tool ( ---------- ) with a native Python-based solution within Google Cloud.

Key Responsibilities

Build and maintain a Python-based ETL pipeline to extract data from a call tracking API
Load and structure data in BigQuery for reporting and analytics
Replace ---------- workflows with custom scripts and automation
Ensure data accuracy, validation, and consistency across the pipeline
Implement scheduling using cloud-native tools (e.g., Cloud Scheduler, Cloud Functions, or Cloud Run)
Optimize query performance and data schema in BigQuery
Integrate clean datasets into Looker Studio dashboards
Monitor pipeline performance and implement logging/error handling

Technical Requirements

Strong experience with Python (requests, pandas, APIs, JSON handling)
Hands-on experience with Google Cloud Platform (BigQuery, Cloud Functions, Cloud Run)
Experience building ETL/ELT pipelines
Familiarity with REST APIs and authentication methods
Experience with data visualization tools (Looker Studio preferred)
Understanding of data modeling and schema design in BigQuery

Nice to Have

Experience replacing tools like ---------- , Zapier, or n8n
Knowledge of call tracking platforms (CallRail, CallTrackingMetrics, etc.)
Experience with logging/monitoring (Cloud Logging)
Recommended Architecture (Best Practice)

Instead of ---------- , use a native Google Cloud pipeline:

Option 1 (Lean + Cost Efficient)

Python script (API pull)
Deploy via Cloud Functions
Trigger with Cloud Scheduler (e.g., every 1044 or {24 hours} minutes or hourly)
Write directly to BigQuery

Option 2 (More Scalable)

Python script in Cloud Run (containerized)
Cloud Scheduler triggers HTTP endpoint
Data processed and inserted into BigQuery