Data Warehouse & ETL for Marketing
Build the data foundation that powers all marketing analytics and AI. We design and implement marketing data warehouses with automated ETL pipelines that extract data from every martech tool, transform it into analysis-ready models, and load it into a central warehouse for unified reporting and activation.
What's Included
Data Warehouse Design
Purpose-built warehouse architecture for marketing analytics on BigQuery, Snowflake, or Redshift.
ETL Pipeline Development
Automated data extraction from all martech tools with transformation rules and quality checks.
dbt Data Modeling
Build clean, documented, tested data models using dbt for consistent marketing analytics.
Reverse ETL
Push warehouse insights back to operational tools u2014 enrich CRM, trigger campaigns, update audiences.
Data Quality Monitoring
Automated data quality checks, freshness monitoring, and alerting for broken pipelines.
Cost Optimization
Warehouse query optimization, partitioning, and cost management to keep cloud bills reasonable.
Platforms & Technologies
Warehouses
ETL/ELT
Transformation
Real-World Results
Marketing Data Warehouse Build
Data in 15 tools, no unified view, analyst spends 60% of time collecting and cleaning data
BigQuery warehouse with Fivetran + dbt u2014 15 source connectors, 40 data models, self-service
Analyst productivity +3x, time-to-insight from weeks to minutes, unified metrics across all teams
Operational Analytics with Reverse ETL
Warehouse has great data, but CRM and tools are stale, manual list uploads daily
Census reverse ETL pushing segments, scores, and insights from warehouse to HubSpot, Google Ads, Intercom
Real-time data in every tool, eliminated 10 hours/week of manual uploads, campaigns use freshest data
Key Benefits
Single Source of Truth
All marketing data in one place with consistent definitions and metrics.
Self-Service Analytics
Clean, modeled data that any team member can query and analyze.
AI-Ready
Structured data foundation that powers ML models, MMM, and predictive analytics.
Tool Agnostic
Change any martech tool without losing historical data or rebuilding analytics.
Our Process
Architecture Design
Design warehouse schema, data models, and ETL architecture based on analytics requirements.
Pipeline Build
Implement ETL connectors, transformation logic, and data quality checks.
Data Modeling
Build dbt models for marketing entities u2014 campaigns, contacts, deals, channels, and touchpoints.
Operationalize
Deploy monitoring, alerting, documentation, and train team on querying the warehouse.
How We Compare
| Aspect | Traditional | Widelly |
|---|---|---|
| Data | Scattered across tools | Unified in one warehouse with consistent models |
| Analytics | In-tool reporting only | Custom analytics on any combination of data |
| Quality | Hope data is correct | Automated testing and monitoring with alerts |
| Activation | Export CSVs manually | Reverse ETL pushes insights to tools automatically |
FAQ
BigQuery vs Snowflake for marketing data?
What is dbt and why do we need it?
How much does a marketing data warehouse cost?
What is reverse ETL?
Ready to Get Started?
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