Design a cross-product user journey data architecture

WHAT THIS TESTS: This question evaluates your ability to design a Customer Data Platform that aggregates and organizes customer data from independently developed products to build unified user profiles without creating tight coupling or privacy violations. The interviewer cares about separation of concerns between data producers and consumers, handling identity across disconnected login systems, and governing PII under regulations like GDPR and CCPA. Success requires balancing centralization with product autonomy.

A GOOD ANSWER COVERS: First, an ingestion layer using lightweight SDKs or API gateways that emit standardized event schemas into a streaming bus like Kafka or Kinesis, keeping product teams decoupled from downstream systems. Second, a central data lake or lakehouse that stores raw events in open formats like Parquet or Iceberg, enabling both batch and real-time analytics. Third, an identity resolution service that maintains an ID graph mapping anonymous IDs, device IDs, and authenticated user IDs using deterministic stitching where possible and probabilistic matching as a fallback, with clear merge and unmerge rules. Fourth, a privacy architecture that isolates PII in encrypted vaults, attaches consent tokens to every event, and propagates deletion requests back to source products via tombstoning or hard deletes. Fifth, schema governance through a central registry with versioning, backward compatibility checks, and data contracts so independent product teams can evolve their telemetry without breaking the analytics pipeline.

COMMON WRONG ANSWERS: Proposing a single shared relational database across all products immediately signals a lack of distributed systems experience. Treating identity resolution as a simple SQL join on email address ignores cross-device behavior and data quality issues. Suggesting that privacy can be handled later in a data warehouse layer fails compliance requirements because raw logs often contain PII. Recommending a rigid global schema forces every product team into a bottleneck, killing adoption.

LIKELY FOLLOW-UPS: How would you handle a user requesting deletion across fifty microservices? What happens when two products disagree on whether two user records represent the same person? How do you maintain analytics uptime when a product team deploys a breaking schema change? What is your latency budget for real-time journey analytics versus backfill processing?

ONE CONCRETE EXAMPLE: Imagine a user browses Product A anonymously on mobile, then logs into Product B on desktop with the same email. Your ingestion pipeline captures both event streams. The identity graph detects the shared email and creates a unified profile, but only after consent is verified in both product privacy vaults. When the user later requests deletion, the vault orchestrates deletion commands back to both product event stores and removes the stitched graph edges, leaving aggregated analytics intact but scrubbing personal identifiers.

Source: Wikipedia: Customer data platform