Marketing Data Infrastructure in Seattle
Modern marketing depends on data for personalization, attribution, automation, and optimization. Yet many organizations struggle with fragmented marketing technology stacks where data remains siloed across platforms, inconsistently defined, and difficult to access for analysis. Building effective marketing data infrastructure requires both technical expertise and deep understanding of marketing measurement needs.
Based in Seattle's technology corridor, I help organizations build the data foundations required for sophisticated marketing analytics and activation. My approach combines analytics engineering best practices with real-world experience from Technical Engagement Measurement teams at major technology companies.
Data Pipeline Development
Automated ETL processes that consolidate marketing data from advertising platforms, CRM, and web analytics into unified data warehouses.
Platform Integration
Connect disparate marketing tools through APIs, webhooks, and reverse ETL to enable cross-platform data activation.
Customer Data Platform
Build unified customer profiles that combine behavioral, transactional, and demographic data for segmentation and personalization.
Data Governance
Establish data quality monitoring, privacy compliance frameworks, and access controls for marketing data assets.
Analytics Engineering
Transform raw marketing data into analysis-ready models using modern data stack tools like dbt and BigQuery.
Measurement Infrastructure
Foundational data architecture for attribution modeling, customer journey analysis, and marketing effectiveness measurement.
The Marketing Data Challenge
Marketing organizations typically operate dozens of specialized tools: advertising platforms, email marketing systems, CRM software, web analytics, A/B testing tools, and more. Each platform maintains its own data silo with unique schemas, update frequencies, and API limitations. This fragmentation creates fundamental challenges for cross-channel attribution, customer journey analysis, and unified reporting.
Additionally, privacy regulations like GDPR and CCPA require careful data governance, while cookie deprecation demands first-party data strategies. Organizations need marketing data infrastructure that addresses these realities, consolidating siloed data while respecting privacy boundaries and enabling flexible analysis.
Data Infrastructure Services
My marketing data infrastructure practice focuses on building scalable, maintainable data systems that serve both analytics and activation use cases. Core services include:
- Marketing data warehouse design and implementation (BigQuery, Snowflake, Redshift)
- Automated data pipelines using modern ETL/ELT tools (Fivetran, Stitch, Airbyte)
- API integration development for advertising platforms and MarTech tools
- Server-side tracking implementation for first-party data collection
- Data transformation and modeling using dbt (data build tool)
- Customer data platform (CDP) implementation and customization
- Reverse ETL setup for data activation back to marketing platforms
- Real-time data streaming for time-sensitive marketing use cases
Modern Data Stack Implementation
The modern data stack has transformed how organizations manage marketing data. Cloud data warehouses provide scalable storage and compute, ETL tools eliminate custom integration maintenance, and analytics engineering frameworks bring software engineering practices to data transformation. I help organizations adopt these technologies strategically, selecting tools that match requirements and team capabilities.
Customer Data Unification
Effective marketing requires unified customer views that connect online behavior, offline purchases, support interactions, and demographic attributes. I build identity resolution systems that probabilistically and deterministically match customer records across data sources, creating comprehensive profiles for segmentation, personalization, and lifetime value analysis.
Data Governance and Privacy
Marketing data infrastructure must respect privacy regulations while enabling business value. I implement data governance frameworks including consent management integration, data retention policies, access controls based on role and data sensitivity, and audit logging for compliance verification. These controls protect customer privacy without preventing legitimate marketing analytics.
Analytics Engineering Approach
Raw marketing data requires significant transformation before analysis. I employ analytics engineering best practices using tools like dbt to build tested, documented, version- controlled data transformations. This approach creates reliable, reusable data models for attribution, customer journey analysis, campaign reporting, and executive dashboards.
Measurement Infrastructure
Marketing data infrastructure should explicitly serve measurement needs. I design data architectures that enable sophisticated attribution modeling, cohort analysis, incrementality testing, and marketing mix modeling. This requires careful consideration of granularity, historical data retention, and computational efficiency for complex analytical workloads.
Seattle Technology Ecosystem
Seattle's concentration of major technology companies has fostered sophisticated approaches to data infrastructure and analytics. Working in this environment has provided exposure to enterprise-scale data challenges and modern solutions. I bring this experience to organizations seeking to build professional-grade marketing data capabilities, whether starting from scratch or evolving existing infrastructure.
Scalable, Maintainable Solutions
Marketing data infrastructure is not a one-time implementation; it requires ongoing maintenance as marketing technology evolves and business requirements change. My approach emphasizes sustainability through clear documentation, modular architecture, automated testing, and team training. The goal is infrastructure that your organization can operate and evolve independently, not systems that create permanent external dependencies.
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