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Project Duration
March 2025 - Ongoing

Client Industry
Digital Marketing - SEO
Target Markets
Germany, France

Job Cloud

Centralized Data Infrastructure for Regional Email Performance Insights and Scalable Marketing Intelligence

Technology Stack

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Client Overview

Challenges in Modeling Subscription and Business Metrics

JobCloud is a leading Swiss digital recruitment platform that connects job seekers with employers through innovative online job portals and matching technology. Operating across multiple regions, JobCloud offers tailored hiring solutions and labor market insights to help businesses attract the right talent while empowering candidates with access to localized and relevant job opportunities.

Solutions Delivered

Full Revamp of Marketing Attribution Model

Automated SEO Insights Pipeline Scalable ETL Framework for API-Based Enrichment

Modular Transformation Layer for Future-Proofing

Team Composition

Data Engineers

GCP Cloud Architects

Python Developers

Marketing nalytics Consultants


Engagement Type

Contractual Hiring


Key Challenges

Key Challenges in Campaign Tracking and Data Quality

Inconsistent GA Attribution

Legacy and modern data sources (GA3, GA4, GTM) produced conflicting attribution results.


Manual SEO Collection

Manual keyword research slowed down content strategy and regional market tracking.


Limited Pipeline Visibility

Lack of error tracking and logging made maintenance and debugging reactive and time-consuming.


Strategic Roadmap

The project kicked off with a full rebuild of the attribution model using consistent transformation logic in BigQuery. A modular approach ensured that attribution rules could be easily updated as campaigns evolved. In parallel, an automated SEO pipeline was launched using Python and DataforSEO’s APIs to track search trends across French and German job-related keywords. All components were designed with scale, security, and traceability in mind.

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To address inconsistencies in attribution and streamline SEO operations, we implemented a fully modular semantic layer in BigQuery, unifying GA3, GA4, and GTM data under consistent logic. Python-based ETL pipelines were built to automate keyword tracking using the DataforSEO API, orchestrated via Cloud Scheduler.

Execution Approach

Modular Attribution and Scalable SEO Data Infrastructure

To address inconsistencies in attribution and streamline SEO operations, we implemented a fully modular semantic layer in BigQuery, unifying GA3, GA4, and GTM data under consistent logic. Python-based ETL pipelines were built to automate keyword tracking using the DataforSEO API, orchestrated via Cloud Scheduler.
Monitoring was improved through centralized logging in Google Cloud Storage, enabling faster error resolution and increased observability.
The entire architecture was designed with scalability in mind—parameter-driven components allow for easy expansion across new rules, markets, or APIs without disrupting existing logic. This solution enhanced data accuracy, operational speed, and long-term maintainability.

BUSINESS IMPACT

Restored Trust in Marketing Attribution
Expanded SEO Visibility Across EU Markets
Improved Pipeline Stability
Accelerated Market Integration

90%+

Accuracy in attribution consistency across all marketing sources


8

hours/week saved from manual keyword research and pipeline troubleshooting


2

international regions covered with automated SEO insights