Analytics Platforms and Dashboards Questions

Comprehensive knowledge of analytics platforms, implementation of tracking, reporting infrastructure, and dashboard design to support marketing, product, and content decisions. Candidates should be able to describe tool selection and configuration for platforms such as Google Analytics Four, Adobe Analytics, Mixpanel, Amplitude, Tableau, and Looker, including the trade offs between vendor solutions, native platform analytics, and custom instrumentation. Core implementation topics include defining measurement plans and event schemas, event instrumentation across web and mobile, tagging strategy and data layer design, Urchin Tracking Module parameter handling and cross domain attribution, conversion measurement, and attribution model design. Analysis and reporting topics include funnel analysis, cohort analysis, retention and segmentation, key performance indicator definition, scheduled reporting and automated reporting pipelines, alerting for data anomalies, and translating raw metrics into stakeholder ready dashboards and narrative visualizations. Integration and governance topics include data quality checks and validation, data governance and ownership, exporting and integrating analytics with data warehouses and business intelligence pipelines, and monitoring instrumentation coverage and regression. The scope also covers channel specific analytics such as search engine optimization tools, social media native analytics, and email marketing metrics including delivery rates, open rates, and click through rates. For junior candidates, demonstration of fluency with one or two tools and basic measurement concepts is sufficient; for senior candidates, expect discussion of architecture, pipeline automation, governance, cross functional collaboration, and how analytics drive experiments and business decisions.

EasyTechnical

0 practiced

Describe step-by-step how you would define measurable KPIs for a new acquisition marketing campaign whose goal is to increase trial signups. Include unit of analysis, primary and secondary KPIs, guardrail metrics, instrumentation requirements, and how you would report progress to stakeholders.

MediumTechnical

0 practiced

Describe how you would implement automated anomaly detection and alerting for core KPIs such as DAU and conversion rate. Include algorithm choices (statistical vs ML), how to handle seasonality and trends, thresholding and suppression strategies to reduce noisy alerts, alert routing, and how to provide context to help triage.

HardTechnical

0 practiced

A Looker dashboard reports CTR = 3% while Amplitude reports CTR = 4.5% for the same time window. Provide a checklist of SQL queries and technical checks you would run to reconcile the discrepancy and identify the root cause (include sampling checks, event deduplication, timezone and filter alignment, denominator definitions, and measurement differences).

EasyTechnical

0 practiced

Define a client-side data layer for a web application. Provide an example JSON-like schema for a product click event (use single quotes to show names) and explain which fields you would mark mandatory, which optional, how you would include versioning, and how to handle property type changes and backwards compatibility.

Example schema:

{
  'event_name': 'product_click',
  'user': {'user_id': null, 'anonymous_id': 'uuid'},
  'page': {'url': 'https://...', 'referrer': 'https://...'},
  'product': {'sku': 'ABC123', 'name': 'T-shirt', 'price': 29.99},
  'timestamp': '2025-01-01T12:00:00Z',
  'properties': {'position': 2, 'experiment_variant': 'B'},
  'schema_version': 1
}

HardSystem Design

0 practiced

Design a solution to join server-side authoritative purchase events (billing system) with client-side analytics events to compute funnel conversions and attribution while avoiding double counting and handling delayed server events. Discuss event schema, canonical keys (order_id), deduplication strategy, buffering, reconciliation jobs, and how to handle unmatched records.

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