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Investigating analytics vs database count gaps
Causes include ad-blocker loss, differing identity logic, timezone mismatches, filtering, and pipeline delay; investigate by aligning definitions and tracing one user.
Why do our analytics and backend user counts not match?
This tests your ability to systematically debug data integrity issues. A great answer first defines the metric, then investigates tracking implementation, privacy blockers, and time zone settings. A red flag is blaming one tool without a structured plan.
Why don't analytics and backend user counts match?
This tests your systematic debugging and understanding that 'user' has different definitions. A good answer first defines 'user' in each system, then investigates tracking implementation, timing differences, and filtering.
Bundled analytics vs warehouse-native trade-offs
Warehouse-native gives one source of truth and SQL flexibility but shifts modeling, performance, and UX onto your team; bundled tools are turnkey but siloed.
Trade-offs: Bundled Analytics vs. a Warehouse-Native Stack?
This tests your grasp of modern data stack trade-offs: cost, data governance, and flexibility. Discuss the pros of warehouse-native (unified data, lower cost, security) vs. the cons (loss of specialized UI, implementation complexity).
Trade-offs: Bundled vs. Warehouse-Native Product Analytics
This tests your grasp of modern data stack architecture. A great answer weighs trade-offs in data control, cost, query flexibility, and team capabilities. A red flag is ignoring total cost of ownership and engineering overhead for a warehouse-native setup.
Building a conversion funnel in SQL
Count distinct users reaching each ordered step, compute step-over-step conversion; the biggest drop-off is the lowest consecutive ratio.
Build a SQL query for a multi-step conversion funnel
Tests your ability to translate a product question into robust SQL. A great answer uses CTEs or left joins to count users at each step, defining the attribution model (e.g., first-touch) and time windows. A red flag is a naive query that double-counts users.
How would you build a SQL query for a conversion funnel?
Tests your ability to translate a business need into a technical SQL solution. A good answer uses CTEs or LEFT JOINs to model sequential steps, counts users at each stage, and discusses attribution.
Client vs server tracking: pros, cons, examples
Client-side wins on UI context but loses data to blockers and tampering; server-side wins on reliability and trust but misses pure UI events.
Client-Side vs. Server-Side Event Tracking: Pros and Cons
Tests your grasp of data integrity trade-offs. A good answer defines both, contrasts reliability vs. implementation ease, and gives clear examples like 'payment_processed' (server) vs. 'button_click' (client). Red flag: Ignoring ad-blockers and data loss.
Client-Side vs. Server-Side Event Tracking
Tests your grasp of data integrity and architectural trade-offs. A great answer defines both, favors server-side for reliability (avoids ad-blockers), but notes client-side's richness for UI events. A red flag is presenting them as equal choices.
Calculating Daily Active Users in SQL
Need per-event user_id and timestamp and a clear active definition; count distinct user_id within the day in a fixed timezone.
Calculate Daily Active Users (DAU) with SQL
This tests your ability to translate a business metric into a precise technical definition and query. A good answer defines "active," specifies the event data needed (user_id, timestamp, event_name), and uses COUNT(DISTINCT user_id).
Calculate Daily Active Users (DAU) with SQL
This tests product sense and SQL fundamentals. Define 'active' with a core product action, describe the event data needed, then write a COUNT(DISTINCT user_id) query. A red flag is writing SQL before defining the business logic for 'active'.
Find leading indicators of long-term churn
Cohort renewers vs churners, compare first-30-day engagement depth and breadth, validate correlations and check causality.
How would you find leading indicators for long-term churn?
Tests your ability to connect a lagging business KPI to leading product metrics. A good answer defines churned/retained cohorts, analyzes first 30-day engagement differences (e.g., feature adoption), and validates findings. A red flag is jumping to ML models.
Find leading indicators for long-term churn
This tests your ability to translate a business problem into a data investigation. A strong answer defines churned vs. retained cohorts, hypothesizes key early behaviors, and compares their frequency to find a leading indicator.
Reframe time series for a tree model
Lag and rolling-window features, calendar and cyclical encodings, then split chronologically to avoid leakage.
Transform a Time Series for a Gradient Boosting Model
Tests your ability to convert a sequential problem into a tabular one. A great answer covers creating lagged/rolling features and time-based features (e.g., day of week), and crucially, specifies a time-aware validation split.