Meta·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Meta data science interview with a product analytics question about diagnosing a drop in a core engagement metric. Pretty standard for this type of role but the question has enough layers to trip you up if you're not systematic.

Questions Asked (1)

Instagram's Daily Active Users dropped by 5% over the course of a week. Walk through how you'd figure out what caused it.

Root Cause AnalysisProduct Analytics & Metrics

Author's notes

My first instinct was to jump straight into segmentation (platform, region, age group) but I forgot to check the obvious stuff first, like whether it was a data pipeline issue or a logging bug.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the metric definition and scope (e.g., DAU definition, time period, platform, region) to ensure you're solving the right problem. Then systematically break down potential causes across internal changes, external events, and data quality issues, prioritizing hypotheses by likelihood and impact. Finally, propose a data-driven investigation plan using logs, experiments, and dashboards to validate or eliminate each hypothesis.

Pro tip: Demonstrate a bias for action by suggesting quick checks (e.g., recent deployments, A/B tests, or data pipeline health) while also outlining a structured long-term investigation. This shows you can balance immediate debugging with rigorous analysis.

1. Clarify the metric and scope

Ask questions to understand how DAU is defined (e.g., logged-in users, unique visitors), the time window (7-day drop), and any segmentation (platform, region, user cohort). This ensures you're analyzing the correct metric and can narrow down the cause.

2. Check for data quality issues

Verify that the drop is real and not due to instrumentation bugs, logging errors, or pipeline failures. Compare with other metrics (e.g., MAU, session duration) and check data freshness and completeness.

3. Segment the data to localize the drop

Break down DAU by dimensions such as platform (iOS/Android/Web), region, app version, user demographics, and acquisition channel. Identify which segments are driving the decline to narrow the scope.

4. Correlate with internal and external events

Look for recent changes (e.g., product updates, algorithm changes, marketing campaigns) and external factors (e.g., holidays, competitor launches, media coverage). Use time-series analysis to see if the drop aligns with specific events.

5. Form and test hypotheses

Prioritize potential causes based on impact and likelihood, then design experiments or queries to validate them. For example, if a new feature caused a crash, check crash logs; if a marketing campaign ended, analyze acquisition data.

Key Points to Mention

Define DAU precisely and consider alternative metrics (e.g., WAU, MAU) to contextualize the drop.
Check for data pipeline issues, logging errors, or changes in tracking that could cause false drops.
Segment by platform, region, app version, and user cohorts to isolate the affected population.
Correlate with recent product releases, A/B tests, or infrastructure changes that might impact user engagement.
Consider external factors like seasonality, holidays, competitor actions, or press coverage.
Propose a systematic approach: start with quick checks, then deep-dive into logs and experiments, and finally validate with statistical methods.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.