Building a Modern Data Stack: E-Commerce ETL Pipeline (Practical Example)

Project Overview

In this case study, I'll walk through how I designed and implemented a scalable data pipeline for an e-commerce company, integrating multiple data sources into a unified analytics platform using Google BigQuery. The solution automated data collection from Shopify, Google Analytics, Klaviyo (email marketing), and DHL shipping systems, enabling real-time business intelligence and automated reporting.

Business Challenge

The client faced several data-related challenges:

• Manual consolidation of shipping costs from daily DHL CSV files

• Siloed customer data across e-commerce, marketing, and analytics platforms

• Lack of real-time visibility into key metrics like Customer Acquisition Cost (CAC)

• Time-intensive monthly reporting process requiring data from multiple sources

Solution Architecture

Data Sources

• Shopify: Transactional data, customer profiles, and inventory

• Google Analytics 4: User behavior, session data, and conversion metrics

• Klaviyo: Email campaign performance and customer engagement

• DHL: Daily shipping costs and logistics data

Technology Stack

• Data Warehouse: Google BigQuery

• ETL/ELT Tools:

  • Airbyte for API-based integrations

  • Power Automate for email automation

  • Google Sheets as an intermediate layer

• Orchestration: Native BigQuery scheduling

• Business Intelligence: Power BI

Implementation Details

1. DHL Shipping Cost Integration

Automated Workflow

1. Email Processing: Power Automate monitors a designated inbox for DHL's daily CSV files

2. Data Staging:

   • Automatic creation of dated tabs in Google Sheets

   • Header standardization and initial data cleaning

3. BigQuery Integration:

   • Connected Sheets syncing to bronze layer tables

   • Partition-based storage optimization

Key Features

• Fully automated process requiring zero manual intervention

• Built-in error handling and notification system

• Audit trail maintenance for compliance

2. Multi-Source Data Integration

Airbyte Pipelines

• Shopify Integration:

  • Hourly synchronization of order data

  • Full historical load with incremental updates

  • JSON payload preservation for audit purposes

• Google Analytics 4:

  • Daily batch processing of user behavior data

  • Custom session stitching logic

  • Bot filtering and data quality checks

• Klaviyo Email Marketing:

  • Near real-time campaign performance tracking

  • Customer engagement metrics integration

  • A/B test results analysis

3. Data Modeling & Transformation

Bronze Layer (Raw Data)

• Preserves source system data in original format

• Implements basic data type standardization

• Maintains historical records for auditing

Silver Layer (Standardized Data)

• Data Cleaning:

  • Deduplication of order records

  • Standardization of shipping zones

  • Currency normalization

• Entity Resolution:

  • Customer identity matching across platforms

  • Order-tracking number linkage

  • Campaign attribution mapping

Gold Layer (Business Ready)

• Analytical Models:

  • Customer 360° view

  • Financial performance metrics

  • Operational efficiency indicators

• Pre-Aggregated Tables:

  • Daily revenue summaries

  • Campaign performance metrics

  • Shipping cost analysis by region

Business Impact

1. Operational Efficiency

• Reduced reporting time from 3 days to 2 hours

• Automated 95% of manual data processing tasks

• Real-time visibility into shipping costs and delays

2. Financial Benefits

• 15% reduction in shipping costs through data-driven zone optimization

• Improved campaign ROI tracking

• Better inventory management through integrated data views

3. Customer Experience

• Enhanced customer segmentation capabilities

• Improved marketing campaign targeting

• Better prediction of shipping delays

Technical Optimizations

Performance Improvements

• BigQuery table partitioning by date

• Clustering on frequently queried columns

• Materialized views for common analysis patterns

Cost Optimization

• Query cost reduction through proper partitioning

• Automated cleanup of temporary tables

• Smart data retention policies

Lessons Learned & Best Practices

1. Data Quality

• Implement thorough validation at ingestion

• Maintain source data integrity

• Regular data quality audits

2. Scalability

• Design for 10x data volume

• Modular pipeline architecture

• Robust error handling

3. Maintenance

• Comprehensive documentation

• Monitoring and alerting

• Regular performance reviews

Conclusion

This project demonstrates how modern data stack technologies can transform e-commerce operations through automated data integration and analytics. The solution not only eliminated manual data processing but also provided new insights that directly impacted the bottom line.

The architecture's success lies in its scalability, reliability, and ability to adapt to changing business needs while maintaining data integrity and performance.