Data Engineer — Amazon Analytics Pipeline (BigQuery, SellerLabs, dbt, Looker Studio)

About the Project We are a multi-brand Amazon seller with 1,000+ SKUs looking to build a reliable, professional data infrastructure for our Amazon channel. We currently use SellerLabs for our Amazon data and Google Sheets for our catalogue, COGS, and supply chain data. We have existing Looker dashboards but they are fragile because they query SellerLabs directly — we need to fix this properly. The goal is to build a solid three-layer data stack: nightly extraction into Google BigQuery, clean data transformations, and reliable dashboards on top. We also want to connect Claude AI directly to BigQuery so we can query our data conversationally in plain English without always relying on a dashboard. This is a build project with a light ongoing retainer after completion for maintenance. We are based in Spain and comfortable working with Asian/Indian timezone overlap. What We Need Built Layer 1 — Data Extraction Pipeline Nightly automated extraction from SellerLabs (SQL and/or API access available) and Google Sheets into Google BigQuery. The pipeline must be stable, scheduled, and alerting on failures. Layer 2 — Data Transformations Clean, documented SQL transformation models (preferably using dbt) that produce reliable, business-ready tables for our four reporting areas: Sales, Profitability, PPC, and Inventory. Layer 3 — Dashboards and AI Querying Four dashboards built in Looker Studio on top of BigQuery covering Sales, Profitability, PPC, and Inventory. Additionally, connect Claude AI to BigQuery via MCP so we can query our data conversationally. The Four Dashboards in Scope Sales — revenue, units, average selling price by brand and SKU, period-over-period comparisons. Profitability — contribution margin per SKU incorporating COGS from Google Sheets, FBA fees, and ad spend. PPC — ACOS, TACOS, ad spend, ROAS by campaign, ad group, and SKU. Inventory — days of cover, reorder alerts, stranded inventory, inbound shipment status. Required Skills and Experience Google BigQuery — schema design and query optimisation Python — for API extraction and pipeline scripting dbt (data build tool) — preferred for transformation layer SellerLabs or Amazon SP-API / Advertising API experience Google Sheets API integration Looker Studio dashboard building Experience designing data pipelines for Amazon seller data at scale Strong written English and proactive communication Nice to Have Experience with Airbyte or similar extraction tools Familiarity with MCP (Model Context Protocol) or AI API integrations Previous work on multi-brand Amazon seller setups Project Structure Phase 1 — Proof of concept (paid, fixed fee, 2–3 weeks): Connect SellerLabs and Google Sheets data into BigQuery, build one transformation model, and rebuild the Sales dashboard on top. This phase validates the approach before full commitment. Phase 2 — Full build (3–4 months): Complete all four dashboards, full transformation layer, AI querying setup, documentation. Phase 3 — Ongoing retainer: Approximately 5–10 hours per month for maintenance, schema updates, and pipeline fixes. To Apply, Please Answer These Questions Have you worked with dbt for managing SQL transformations, or do you handle transformations directly in Python or SQL? Walk us through your preferred approach. How would you approach the nightly extraction from SellerLabs into BigQuery — what method or tooling would you use? Have you designed a BigQuery schema for Amazon seller data before? Briefly describe the structure you used. What is your current availability and are you open to a light ongoing retainer after the build phase?