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How do you measure impact while accounting for the novelty effect?
Tests your ability to design experiments that isolate long-term effects. A good answer proposes a long-running A/B test, analyzing user cohorts by join date to see if initial lift decays. A red flag is ignoring the novelty effect and suggesting a short test.
What data do you need and what steps build a WAU dashboard?
Tests defining a metric, modeling events, and wiring them into a BI tool. A strong answer names the feature event, sets a rolling 7-day window, counts distinct users by period, and configures the BI layer. Red flag: jumping to charts before defining active.
How would you build a weekly active user dashboard?
This tests translating a business need into a technical spec. First, clarify the definition of "active." Then, identify the event data needed (user_id, timestamp). Finally, outline the ETL and BI tool steps. A red flag is not clarifying the metric first.
How would you build a weekly active user dashboard?
This tests your ability to translate a business request into a technical plan. A good answer defines "active," identifies necessary data (user ID, timestamp, event), outlines the data modeling, and explains the BI tool implementation.
Instrument a mobile event and surface it in analytics
This tests full-stack analytics plumbing. A good answer hits: structured client logging, batched transmission, backend validation, warehouse aggregation, and dashboard verification. A red flag is fire-and-forget logging with no schema checks or reconciliation.
Instrumenting a New User Interaction in a Mobile App
Tests your understanding of the end-to-end analytics pipeline. A good answer defines the event, instruments it on the client with an SDK, verifies it in a debug view, and confirms its arrival in the data warehouse after the ETL process.
Instrumenting a New User Interaction for Analytics
Tests your grasp of the full data lifecycle. A good answer covers event definition, client-side implementation, the backend pipeline, and end-to-end verification.
Propose a technical architecture for a centralized Metrics Layer or Metrics Store
This tests your ability to decouple metric semantics from storage and query tools. A strong answer outlines a semantic layer with versioned definitions, a query API, and enforced downstream consumption.
Propose a technical architecture for a centralized Metrics Layer.
This tests your grasp of data governance and semantic layers. A great answer outlines a system with a central definition store (e.g., YAML in Git), a query engine, and an API, ensuring all teams get consistent metric results.
Design a Centralized Metrics Layer
This tests your grasp of data governance and creating a single source of truth. A good answer defines a semantic layer between the data warehouse and BI tools, centralizing metric definitions in code.
How would you design an automated data quality monitoring system?
Tests turning data quality into tiered checks for exec dashboards. Strong answers combine freshness, volume, schema, and distribution validation with severity-based paging. Red flag: static thresholds without noise reduction or business-impact triage.
How would you design a data quality monitoring system?
This tests your systematic approach to data reliability. A strong answer defines quality dimensions (freshness, volume, schema), proposes specific checks, and outlines an alerting strategy. A red flag is listing checks without tying them to business impact.
Design a Data Quality Monitoring & Alerting System
This tests translating business needs into a concrete data validation strategy. A good answer defines checks based on business impact (freshness, volume, schema), then outlines a tiered alerting system. A red flag is naming tools before defining the problem.
Describe the architecture of a generic A/B testing framework
Hash-based user bucketing, config service, pre-registered metrics, and confidence intervals on dashboards.
Design an A/B Testing Framework
This tests your ability to design a scalable system with statistical rigor for non-experts. A great answer outlines config management, deterministic user hashing, a data pipeline for metrics, and a results UI that simplifies stats.
Describe the architecture of an A/B testing framework
This tests your system design skills for experimentation, from user bucketing to statistical analysis. A good answer covers user assignment, a config service, a data pipeline, and a results layer with statistical significance.
Design a self-service analytics platform for non-technical users
Tests separation of semantic modeling, UI, and query generation for safe self-service analytics. Strong answers cover a semantic layer with unified metrics, drag-and-drop UI with AST-based SQL generation, and caching.
Design a self-service analytics platform for non-technical users
Tests your ability to abstract SQL. A great answer outlines a semantic layer for virtual datasets, a no-code drag-and-drop UI, and a backend that translates UI state into SQL queries. A red flag is describing only a SQL editor, ignoring non-technical users.
Design a self-service analytics platform for non-technical users
Tests your ability to design a layered system for non-technical users. A great answer outlines a semantic layer for data modeling, a no-code UI for exploration, and a query generation engine.
How do you root-cause a 20% revenue drop with no pipeline failures?
Reconcile against raw events, slice by dimension for silent gaps, audit schema drift.