The metric definitions looked clean on paper but the SQL got complicated fast.
Start by clarifying the grain and definitions: one row per variant, with assigned users as the denominator for signup rate and D30 retained users as the numerator for retention rate. Build a base table of assigned users per variant, then left join aggregated metrics from exposures, subscription events, and sessions, using conditional aggregation and date logic to compute 7-day trial signups and D30 retention.
Pro tip: Always state your assumptions about metric definitions (e.g., 7-day trial signup = trial started within 7 days of assignment; D30 retention = active on day 30 after signup) and mention that you would validate with a quick sanity check on counts before finalizing the query.
Confirm what 'assigned users', '7-day trial signups', 'D30 retained users', and 'retention rate' mean, and ensure the output is one row per variant. Define the time windows and whether rates are per assigned user or per signup.
Select distinct user_id and variant from the experiment assignments table, filtering to the experiment of interest and the relevant assignment window. This forms the denominator for assigned users and signup rate.
From subscription lifecycle events, identify trial signups within 7 days of assignment. Left join to the base table and count distinct users per variant to get 7-day trial signups, then compute signup rate as signups divided by assigned users.
From app sessions, identify users active on day 30 after signup (or after assignment, depending on definition). Count distinct retained users per variant and compute retention rate as retained users divided by assigned users (or by signups).
Join the aggregated metrics on variant, select the required columns, and ensure one row per variant. Use COALESCE to handle nulls and round rates appropriately.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Basically asking what could go wrong before the SQL even runs.
Start by framing the pipeline as the foundation for trustworthy experimentation, then walk through the data requirements layer by layer: ingestion, transformation, and serving. Emphasize that reliability comes from explicit handling of assignment, exposure, and metric definitions, not just raw data volume.
Pro tip: Always discuss data quality checks and idempotency in the pipeline—these are often overlooked but critical for reproducible A/B test results. Mentioning how you'd handle late-arriving data or duplicate events shows operational maturity.
Ensure the pipeline captures and preserves the unit of randomization (e.g., user_id, session_id) and the variant assignment consistently across all events. This is the backbone for any valid comparison.
Log when a unit is actually exposed to the treatment and any trigger events that qualify them for analysis. Without this, you risk dilution or misattribution of effects.
Implement clear, versioned definitions for each metric (e.g., click-through rate, conversion) and compute them at the unit level before aggregation. This avoids Simpson's paradox and ensures consistency.
Add validation checks for missing values, duplicates, and outliers; handle late-arriving data with watermarks or reprocessing. This guarantees that metrics are computed on a clean, complete dataset.
Store raw data immutably, version transformation logic, and track data lineage so any metric can be recomputed exactly as before. This is essential for debugging and trust.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.