Data Analysis and Requirements Translation Questions

Focuses on translating ambiguous business questions into concrete data analysis plans. Candidates should identify the data points required, define metrics and key performance indicators, state assumptions to validate, design the analysis steps and queries, and explain how analysis results map back to business decisions. This includes data quality considerations, required instrumentation, and how analytical findings influence product requirements or architectural choices.

HardSystem Design

44 practiced

Design an analytics pipeline to support A/B/n experimentation at scale. Requirements: track correct assignment and exposure, collect immutable experiment logs, compute treatment-level metrics with multiple-comparison corrections, support sequential analyses while preventing peeking bias, and enable rolling back or quarantining experiments. Cover data collection, storage, analysis tooling, and monitoring for telemetry quality.

HardTechnical

52 practiced

Describe methods to detect, prevent, and remediate metric leakage or double-counting when multiple ETL jobs or teams may populate overlapping datasets. Cover architectural choices (atomic writes, partition ownership), governance (dataset owners, contracts), tooling (lineage and producer tags), and anomaly detection rules to detect duplicative writes.

EasyTechnical

61 practiced

You're collaborating with a PM to instrument a new 'Share' feature. Describe the collaborative process to define event names, properties to capture (recipient-type, channel, success/failure), privacy constraints, sampling strategy if needed, and success metrics. Explain how you'd prioritize fields to minimize performance impact while keeping analytical usefulness.

HardTechnical

51 practiced

You need to compute per-user dwell time from clickstream events that can arrive out-of-order and be duplicated. Propose an algorithm and pipeline architecture (streaming or hybrid) that handles event-time ordering, deduplication, session timeouts, and late events, while remaining horizontally scalable and providing provisional and final estimates of dwell time.

EasyTechnical

62 practiced

Given an events table schema:

| column | type ||-------------|-------------------------------|| user_id | bigint || event_name | varchar || event_time | timestamp with time zone || properties | jsonb |

Write an ANSI SQL query (Postgres/BigQuery compatible) to compute Daily Active Users (DAU) for the last 14 days grouped by date. Explain how you handle timezones, null user_id, and deduplication of multiple events per user per day.

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