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Building data infrastructure feels overwhelming because you're not building one thing—you're coordinating five different systems that weren't designed to work together. Each system has its own authentication, data format requirements, rate limits, and documentation written for developers. The traditional data infrastructure stack requires expertise in: Tracking layer: GTM server-side (requires Google Cloud Platform), Docker containers, environment variables, custom JavaScript, dataLayer implementation, trigger logic, tag templates. Storage layer: BigQuery (SQL dialect), schema design, partitioning strategies, cost optimization, IAM permissions, service accounts, API credentials. Transformation layer: dbt (data build tool), SQL transformations, incremental models, testing frameworks, version control, CI/CD pipelines. Analysis layer: Looker Studio connections, SQL queries, visualization design, calculated fields, parameter passing, caching strategies. Activation layer: Platform APIs (Facebook CAPI, Google Ads API, TikTok Events API), OAuth flows, webhook configurations, error handling, retry logic. Each layer demands specialized knowledge. Most small businesses don't have developers with this expertise on staff. Hiring...