## Project Overview Seeking an experienced data engineer to integrate our complete Google marketing stack into Google BigQuery as a centralized data warehouse, with an AI-powered natural language query interface on top. ## Scope of Work **Phase 1: Data Integration to BigQuery** Connect and automate data pipelines from: - Google Analytics 4 (GA4) - Google Ads - Google Search Console - Google Tag Manager - Google Merchant Center Requirements: - Set up automated, near real-time data transfers - Design efficient BigQuery schema with proper data modeling - Implement ETL/ELT processes with data quality checks - Create unified views combining data across platforms - Import historical data - Document all data flows and transformation logic **Phase 2: AI Integration Layer** Implement AI-powered interface for natural language querying of BigQuery data. Requirements: - Configure secure connection between AI platform and BigQuery - Set up authentication and access controls - Test and validate query capabilities - Provide user documentation and training session ## Deliverables 1. Fully functional BigQuery data warehouse with all five Google platforms connected 2. Automated data pipeline documentation (schemas, flows, schedules) 3. AI interface configured with connection guide and sample queries 4. User guide for querying data through AI 5. One training session (1-2 hours) ## Required Skills **Must Have:** - 3+ years experience with Google BigQuery - Strong SQL and data modeling expertise - Experience integrating Google Marketing Platform tools - Google Cloud Platform (GCP) familiarity - Experience with data pipeline tools (Fivetran, Stitch, Airbyte, or custom ETL) - API integration knowledge (OAuth 2.0) **Nice to Have:** - Experience with AI/LLM integration layers - dbt (data build tool) knowledge - Google Analytics 4 or Google Ads certifications - Previous work connecting AI tools to data warehouses ## Project Timeline Estimated Duration: 4-6 weeks - Phase 1: 3-4 weeks - Phase 2: 1-2 weeks ## Application Requirements Please include: 1. Relevant experience with similar projects (portfolio/case studies) 2. Proposed technical approach 3. Estimated timeline with milestones 4. Your rate and cost estimate 5. Availability and references