Data Engineer — Google Ads + Amazon Advertising API → BigQuery Pipelines (E-commerce)

Overview We run a German multi-brand e-commerce business (Amazon, Shopify, Otto) with an existing BigQuery data warehouse and a per-SKU contribution-margin model (CM1–CM3). We need an experienced data engineer to build two daily, keyword-level ingestion pipelines — Google Ads and Amazon Advertising (v3) — into BigQuery, and join them to our existing SKU-level margin so we can optimize campaigns on true profit, not just ACOS. This is an extension of a live, well-structured warehouse — not a greenfield build. You must already be fluent in BigQuery, Python, and both ad APIs. What you'll build Amazon Advertising API (v3) daily pipeline — Sponsored Products first — at keyword/target/ASIN grain, with correct attribution-lookback handling (rolling re-sync, idempotent). Google Ads API daily pipeline — keyword + search-term level. A documented mart joining keyword spend → attributed revenue → contribution margin (CM3) per SKU. Scheduling (GCP), secrets in Secret Manager, logging/alerts, and a reconciliation check vs. the ad dashboards. Schema docs + runbook + handover. You must have Proven Google Ads API and Amazon Advertising API (v3) pipeline experience (show examples). Strong Python + BigQuery + Google Cloud (Cloud Run/Functions, Cloud Scheduler, Secret Manager, IAM). Deep understanding of ad attribution windows and incremental/idempotent loads. Solid data modeling + SQL; dbt a plus. E-commerce/marketing data a plus. To apply, please answer briefly: Link or short description of a Google Ads API pipeline and an Amazon Ads API pipeline you built. How do you handle Amazon Ads attribution lookback in an incremental, idempotent load? Which orchestration would you use on GCP, and why? Rough estimate (hours/weeks) and your rate.