Justify and harden your analytics and BI stack
Overview
English summary: This question evaluates a candidate's ability to design, justify, and harden an end-to-end analytics and BI stack—covering ingestion, storage/warehouse, transformation, metrics and semantic layers, data modeling (including SCD2 and late-arriving events), PII handling, data quality, and cross-team operational processes—measuring competencies in system design, data engineering, and analytics productization. It is commonly asked to assess trade-off reasoning around cost, governance, scalability, latency, and maintainability within the Data Manipulation (SQL/Python) domain and tests both conceptual understanding and practical application, including operational considerations such as idempotency and automated quality checks.
List your current analytics tech suite end-to-end (ingestion, storage/warehouse, transformation, orchestration, catalog/lineage, experimentation platform, BI/visualization, and notebook environment). For each layer, justify the choice vs. two alternatives on cost, governance, scalability, latency, and ease of self-serve. Propose a canonical event and experiment data model that supports trustworthy dashboards and ad-hoc analysis: include slowly changing dimensions (SCD2), late-arriving events, idempotent backfills, and PII handling (tokenization/row-level security). Describe your metrics layer (semantic definitions, versioning, change review, owners) and how BI pulls from it to ensure a single source of truth. Outline an automated data quality framework (freshness, schema, distributional drift tests) and a lightweight Python approach to detect breaking changes in metric definitions before deploy. Finally, explain how you enable async collaboration in a remote org (code review, approvals, lineage, incident runbooks) and how you prevent dashboard metric drift across teams.