About the Company.
A leading US based marketing analytics organization managing multi channel paid media performance for multiple brands through a centralized analytics environment. The company relied heavily on accurate, timely measurements of spend, conversions, device level performance, and cross platform attribution delivered through BI dashboards. Their legacy reporting environment, built on Redshift masterviews, had become difficult to scale and maintain, creating inconsistencies as they began adopting Snowflake for modern analytics workloads.
Solution Summary.
Algoscale rebuilt the client’s paid media data pipeline in Snowflake using a clean, layered dbt architecture , fully replicating and enhancing the logic of their legacy Redshift masterviews. The new architecture ingests raw platform data like Facebook Ads, applies consistent transformations, reconciles metrics end-to-end, and delivers a unified “OneView” mart powering downstream dashboards with accurate, device level conversion insights. The enhanced architecture now supports multi platform paid media pipelines including Facebook, Google Ads, Reddit, and other channels through unified ingestion and standardized dbt transformations.
Customer Challenges.
The client faced significant challenges from fragmented contract management and lack of automation
Metric Misalignment Across Environments.
Snowflake dashboards showed inconsistencies with trusted Redshift masterviews especially for DEVICE, ACTIONS/ACTION_VALUES arrays and certain conversion types.Delayed Troubleshooting & Decision Making.
Analysts frequently had to cross check Snowflake vs Redshift numbers manually, slowing down reporting cycles and causing confusion among stakeholders.Opaque Transformation Logic.
Legacy models lacked clear separation between staging, core metrics, and consumption layers, making debugging and enhancements difficult.Lack of Scalable, Future-Ready Architecture.
As the team expanded Snowflake usage they needed a modular, governed structure capable of supporting additional platforms and analytics needs.
Algoscale Solution.
Algoscale delivered a fully reconciled, scalable Snowflake + dbt ecosystem with transparent, maintainable, and validate transformations.
Rebuilt Redshift Logic Using a Clean L1-L4 dbt Layering.
- L1 Staging: Standardized types, normalized naming, extracted platform/device information, and ensured row level consistency.
- L2 Core Metrics: Reshaped daily spend, impressions, clicks, and conversions at business relevant grains.
- L3 OneView Marts- Unified multi platform metrics with aligned dimensions and logic.
- L4 Masterviews- Created BI optimized, consumption ready models mirroring existing Redshift outputs.Device-Level Conversion Recovery.
Restored full visibility into add_to_cart, custom pixel events, and other device level conversions lost in Snowflake’s initial implementation.End-to-End Metric Reconciliation.
- Compared Snowflake vs Redshit at the granularity of date x platform x device x conversion_name.
- Identified and corrected transformation gaps for Facebook ACTIONS, ACTION_VALUES, and CONVERSIONS arrays.Automated Backfills & Incremental Model Strategy.
- Ensured freshness of late arriving data.
- Implemented incremental logic with surrogate keys, optimizing pipeline performance and cost.Environment Management & Observability.
- Separated dev/prod targets with controlled promotion
- dbt tests, schema tests, and validation matrices for continuous monitoring.Expanded Multi-Platform Ingestion Support.
Enabled ingestion across Google Ads, Reddit Ads, and additional paid media platforms using standardized schemas and shared dbt macros.Automated Orchestration & DevOps Integration.
Added Airflow-based scheduling and GitHub Actions for CI/CD, enabling automated model testing, incremental runs, and production deployments.
Algoscale Differentiators.
Deep expertise in dbt architecture, layered modeling, and enterprise grade transformation frameworks.- Proven capability to reconcile legacy systems (Redshift) with modern cloud warehouses (Snowflake).
- Strong understanding of paid media data structures, including platform arrays, conversion actions, and attribution dimensions.
- Emphasis on data quality, transparency, and reproducibility through dbt tests and validation matrices.
- Ability to create scalable, extensible frameworks for adding new ad platforms and analytics use cases.
- Expertise in orchestrated analytics pipelines, using Airflow for automated job scheduling and GitHub Actions for CI/CD, ensuring reliability and reusability across multi-platform ad data sources.
Values Delivered.
Through this engagement, Algoscale delivered measurable improvements:
Zero Unexplained Mismatches.
100% alignment for validated Facebook spend, conversions, and device level metrics across the 30 day reconciliation window.Analyst Confidence Restored.
Teams shifted from reconciling numbers to analyzing performance.Faster Enhancement Cycles.
L1-L4 modular structure reduced debugging and change management effort by ~40%.Recovered 1.2k+ Missing Conversions in One Validation Cycle.
Previously null Snowflake fields now flow accurately from RAW to L4.Future-Ready Analytics Foundation.
The unified OneView mart now supports rapid onboarding of additional platforms.
