Peer Review 3: France Data Engineering Job Market Transformations, Visualization, and Feedback (Part 2)

Introduction Welcome back to the last part peer review of the France Data Engineering Job Market Analysis pipeline. In Part 1, we explored the project’s infrastructure, cloud setup, and orchestration. Now, we’ll go deeper into the heart of the data platform: transformations, data warehouse design, dashboarding, reproducibility, and actionable feedback. 1. Transformations with dbt Modern data engineering pipelines are built on modular, testable transformations—and dbt (Data Build Tool) shines in this space. This project structures its dbt codebase into staging, core, and marts layers, following best practices for maintainability and scalability. Staging Models: Clean and standardize raw job posting data. Core Models: Build core analytical tables, e.g., fact_jobs, dim_company, dim_skills. Marts Models: Deliver analytics-ready tables for direct dashboard consumption, e.g., top skills, salary distribution, remote job trends. Integration: dbt transformations are automated via Kestra, ensuring that new data is transformed and ready for analytics on a regular schedule. Comment: Excellent use of dbt’s modular structure. The pipeline ensures all transformations are reproducible, testable, and production-ready. For further robustness, consider populating the tests/ and macros/ folders with custom tests and logic. 2. Data Warehouse Design The project leverages Google BigQuery as the data warehouse, which is a solid choice for scalable analytics. External Tables: Raw CSVs in GCS are registered as external tables in BigQuery. Native Tables & Marts: Transformed data is materialized as native tables and views for efficient querying. Partitioning & Clustering: While the project’s structure suggests a thoughtful separation between staging and marts, there isn’t explicit documentation of table partitioning or clustering strategies. These can make a big difference in query performance and cost efficiency at scale. Comment: Good warehouse design with clear separation of concerns. Adding documentation and rationale for partitioning and clustering would further strengthen the warehouse layer. 3. Dashboarding & Data Products The final data product is a Power BI dashboard that visualizes key insights: Tiles include: Top skills in demand Salary distribution Remote work trends Company performance and job trends over time The dashboard is visually clear (see screenshots in the images/ folder) and directly queries marts tables in BigQuery, ensuring up-to-date insights. Comment: Strong dashboard implementation. Multiple analytical tiles provide different perspectives for stakeholders, and the visuals are easy to interpret. 4. Reproducibility & Documentation Reproducibility is a cornerstone of engineering excellence. This project excels in that area: README.md includes step-by-step instructions for everything—infra setup, data ingestion, dbt transformations, and dashboard connection. Sample config variables are provided, and the logical flow is easy to follow. Comment: Clear, actionable documentation makes this project easy to run and adapt. Excellent work! 5. Actionable Feedback & Areas for Growth Even great projects have room to grow! Here are some opportunities for further improvement and learning: Data Warehouse Optimization: Explicitly document partitioning and clustering strategies in BigQuery marts. Explain how these optimize for cost and performance. Testing & CI/CD: Add dbt tests for data quality (e.g., uniqueness, null checks) and consider adding pipeline-level validation. Explore integrating CI/CD (e.g., GitHub Actions) for automated testing and deployment. Workflow Transparency: Include diagrams or screenshots of Kestra flows in the documentation for better orchestration visibility. Streaming Ingestion (Optional): If real-time job data becomes available, consider building a streaming ingestion pipeline to expand the project’s scope. Leverage dbt Advanced Features: Utilize the empty macros/, tests/, and snapshots/ directories for more advanced dbt features, such as custom logic or snapshotting slowly changing dimensions. Conclusion: Reviewing and learning from real-world projects is one of the best ways to grow as a data engineer. This project is a fantastic example of a modern, cloud-native data engineering pipeline—well-documented, automated, and designed for actionable analytics. Key takeaways: Modular, testable transformations with dbt are the backbone of maintainable analytics pipelines. Clear separation between raw, staging, and marts layers makes analytics scalable and robust. Visualization is more than pretty charts—it’s about surfacing real insights for stakeholders. Great documentation is as important as great code. What’s next? If you enjoyed this review, try evaluating an open-source project yourself and utilize the learning opportunities pr

May 2, 2025 - 15:29

Peer Review 3: France Data Engineering Job Market Transformations, Visualization, and Feedback (Part 2)

Introduction

Welcome back to the last part peer review of the France Data Engineering Job Market Analysis pipeline. In Part 1, we explored the project’s infrastructure, cloud setup, and orchestration. Now, we’ll go deeper into the heart of the data platform: transformations, data warehouse design, dashboarding, reproducibility, and actionable feedback.

1. Transformations with dbt

Modern data engineering pipelines are built on modular, testable transformations—and dbt (Data Build Tool) shines in this space. This project structures its dbt codebase into staging, core, and marts layers, following best practices for maintainability and scalability.

Staging Models: Clean and standardize raw job posting data.
Core Models: Build core analytical tables, e.g., fact_jobs, dim_company, dim_skills.
Marts Models: Deliver analytics-ready tables for direct dashboard consumption, e.g., top skills, salary distribution, remote job trends.

Integration:

dbt transformations are automated via Kestra, ensuring that new data is transformed and ready for analytics on a regular schedule.

Comment:

Excellent use of dbt’s modular structure. The pipeline ensures all transformations are reproducible, testable, and production-ready. For further robustness, consider populating the tests/ and macros/ folders with custom tests and logic.

2. Data Warehouse Design

The project leverages Google BigQuery as the data warehouse, which is a solid choice for scalable analytics.

External Tables: Raw CSVs in GCS are registered as external tables in BigQuery.
Native Tables & Marts: Transformed data is materialized as native tables and views for efficient querying.

Partitioning & Clustering:

While the project’s structure suggests a thoughtful separation between staging and marts, there isn’t explicit documentation of table partitioning or clustering strategies. These can make a big difference in query performance and cost efficiency at scale.

Comment:

Good warehouse design with clear separation of concerns. Adding documentation and rationale for partitioning and clustering would further strengthen the warehouse layer.

3. Dashboarding & Data Products

The final data product is a Power BI dashboard that visualizes key insights:

Tiles include:
- Top skills in demand
- Salary distribution
- Remote work trends
- Company performance and job trends over time

The dashboard is visually clear (see screenshots in the images/ folder) and directly queries marts tables in BigQuery, ensuring up-to-date insights.

Comment:

Strong dashboard implementation. Multiple analytical tiles provide different perspectives for stakeholders, and the visuals are easy to interpret.

4. Reproducibility & Documentation

Reproducibility is a cornerstone of engineering excellence. This project excels in that area:

README.md includes step-by-step instructions for everything—infra setup, data ingestion, dbt transformations, and dashboard connection.
Sample config variables are provided, and the logical flow is easy to follow.

Comment:

Clear, actionable documentation makes this project easy to run and adapt. Excellent work!

5. Actionable Feedback & Areas for Growth

Even great projects have room to grow! Here are some opportunities for further improvement and learning:

Data Warehouse Optimization:
- Explicitly document partitioning and clustering strategies in BigQuery marts. Explain how these optimize for cost and performance.
Testing & CI/CD:
- Add dbt tests for data quality (e.g., uniqueness, null checks) and consider adding pipeline-level validation.
- Explore integrating CI/CD (e.g., GitHub Actions) for automated testing and deployment.
Workflow Transparency:
- Include diagrams or screenshots of Kestra flows in the documentation for better orchestration visibility.
Streaming Ingestion (Optional):
- If real-time job data becomes available, consider building a streaming ingestion pipeline to expand the project’s scope.
Leverage dbt Advanced Features:
- Utilize the empty macros/, tests/, and snapshots/ directories for more advanced dbt features, such as custom logic or snapshotting slowly changing dimensions.

Conclusion:

Reviewing and learning from real-world projects is one of the best ways to grow as a data engineer. This project is a fantastic example of a modern, cloud-native data engineering pipeline—well-documented, automated, and designed for actionable analytics.

Key takeaways:

Modular, testable transformations with dbt are the backbone of maintainable analytics pipelines.
Clear separation between raw, staging, and marts layers makes analytics scalable and robust.
Visualization is more than pretty charts—it’s about surfacing real insights for stakeholders.
Great documentation is as important as great code.

What’s next?

If you enjoyed this review, try evaluating an open-source project yourself and utilize the learning opportunities provided by Data Talks Club.