Intermediate everything in Analytics & Metrics, page 2
Visualizing a correlation with a third variable
A scatter plot with a trend line shows the relationship; encode network type by color or facets to expose a lurking variable.
Cohort analysis for an onboarding change
A cohort groups users by a shared start trait; compare pre and post Jan-1 signup cohorts on retention by age.
Visualizing a non-linear funnel with re-entry
A linear funnel cannot show branching or re-entry; a Sankey diagram encodes flow volume, splits, and leaks as proportional ribbons.
Enforcing GDPR Right to be Forgotten at scale
Map the subject's data, then crypto-shred the lake, DELETE in the warehouse, and evict caches via an auditable, idempotent workflow.
Designing a useful button_click event payload
Include identity, timestamp, and context plus properties like button id, screen, and state; govern with a naming convention.
Designing a warehouse model for feature adoption
Star schema with a feature-usage fact table at a defined grain, surrounded by user, feature, date, and device dimensions.
Stationarity in time series and why ARIMA needs it
Constant mean/variance/autocovariance; ARIMA's coefficients assume them; test with the ADF test and ACF plots; achieve it via differencing or log transforms.
Describe the architecture of a generic A/B testing framework
Hash-based user bucketing, config service, pre-registered metrics, and confidence intervals on dashboards.
Explain event schemas and why schema registries matter at scale
This tests schema evolution and data contracts in distributed systems. A good answer defines schemas as contracts, explains that a registry enforces compatibility to block breaks, and lists pain like pipeline failures. Never treat schemas as optional docs.
How would you instrument and query P95 API latency by region?
This tests white-box latency instrumentation and safe cardinality for percentile aggregation. Strong answer: emit histograms by region, query P95 with histogram_quantile or a log percentile, and keep trace IDs in logs only.
Which classical baseline model handles weekly seasonality and upward trend?
Tests matching model structure to data characteristics. Name Holt-Winters triple exponential smoothing; map its level, trend, and seasonal equations to weekly period. Red flag: jumping to SARIMA without explaining why ETS is the natural baseline.
Compare data warehouses and data lakes. How does a lakehouse merge benefits?
Tests schema tradeoffs. Warehouses enforce ACID for BI but cost more; lakes store raw cheaply but lack governance. Lakehouses add ACID metadata on object storage to unify ML and BI.
Propose a North Star Metric for a product you know
Definition; your product's metric; how value drives retention and revenue.
Apply AARRR to B2B SaaS vs B2C mobile game analytics
This tests mapping AARRR to instrumentation across business models. A strong answer contrasts B2B account activation and expansion against B2C session-zero funnels and whale monetization. Red flag: same metrics ignoring account hierarchies and ad attribution.
What is the difference between a metric and a KPI?
Tests strategic vs operational measurement discernment. Answer: KPIs track critical goals; metrics track processes. Page views are a metric; conversion rate is the KPI. Red flag: calling all data KPIs or using page views as success proof.
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.
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.
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.
Design a data model for feature adoption tracking
Tests dimensional modeling for high-volume events so PMs can query Feature A not B without complex SQL. A strong answer uses an event fact table plus a materialized user-feature summary. Red flag: a wide user table with boolean columns per feature.
Explain cohort retention and write a pseudo-query for May signups
Tests cohort retention vs aggregate DAU and SQL self-joins for Week 1, 2, and 4 retention from May signups. Strong answers define cohorts by signup date, use datediff, and left-join activity. Red flag: using calendar week instead of relative signup date.
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