A one-pager (technical design)
“Draft a one-pager for the technical design of our user data deletion flow under GDPR. Small SaaS, ~30k users, primary store is Postgres, async deletion via Sidekiq workers, audit log in S3. Goal: 30-day SLA, no orphaned data, deletion verified.”
Typical AI
would get scrolled past
# Technical Design Document: GDPR-Compliant User Data Deletion Pipeline ## Executive Summary This document outlines the comprehensive technical design for implementing a fully GDPR-compliant user data deletion workflow within our SaaS platform. The proposed solution is engineered to ensure complete adherence to Article 17 of the General Data Protection Regulation (the "Right to Erasure"), while maintaining system integrity, performance, and providing a robust audit trail for compliance purposes. ## Background and Context In the current regulatory landscape, GDPR compliance is paramount for SaaS organizations operating in or serving users within the European Union. With approximately 30,000 users on our platform, the implementation of a systematic, reliable data deletion mechanism is critical to mitigate legal risk and maintain user trust. ## Proposed Architecture The proposed architecture leverages our existing infrastructure stack, including PostgreSQL as the primary relational data store, Sidekiq for asynchronous job processing, and AWS S3 for durable audit log storage. The deletion pipeline will follow a multi-stage approach designed to ensure complete data removal while maintaining referential integrity.
RawReply
the one that gets a reply
Technical Design — User Deletion (GDPR) Goal Delete a user's data within 30 days. Nothing orphaned. Audit trail in S3. Flow 1. User requests deletion in-app 2. Email confirmation, 24h cancellation window 3. After window: Sidekiq marks user `deletion_pending`, enqueues per-table workers 4. Workers delete in dependency order (sessions → events → comments → user row) 5. Reconciler runs day after SLA, fails loud if any row references the deleted user_id 6. Audit lands in S3 (object-lock, 7-year retention) Open questions Stripe + Postmark — purge from third parties or sever the join? Backups are 30 days — let deleted data age out, or accelerate? Cost vs strict Article 17 read. Risks Worker fails mid-cascade → idempotent workers + reconciler. Reference leak from a feature we forgot → reconciler catches it. Audit log corruption → object-lock + cross-region replication. Next Sketch worker fan-out + write reconciler this sprint. Decision on backups + third parties before audit log lands.