Covers techniques and practices for ensuring the correctness and reliability of analytical outputs, metrics, and reports. Topics include designing and implementing sanity checks and reconciliations, comparing totals across different calculation methods, validating metrics against known baselines or prior periods, testing edge cases and boundary conditions, and detecting and flagging data quality anomalies such as missing expected data, unexplained spikes or drops, and inconsistent values. Includes methods for designing queries and monitoring checks that surface data quality issues, debugging analytical queries and calculation logic to identify errors and root causes, tracing problems back through data lineage and ingestion pipelines, creating representative test datasets and fixtures, establishing metric definitions and versioning, and automating validation and alerting for metrics in production.
HardSystem Design
0 practiced
A downstream dashboard shows NA or empty for a KPI after an upstream schema change removed a column. How would you detect such schema-breaking changes early? Describe both detection mechanisms (e.g., schema hashes, column presence checks in CI) and automated mitigation or graceful degradation strategies (fallback columns, default values, feature flags) to keep dashboards functional until a permanent fix is deployed.
HardSystem Design
0 practiced
Design a database schema and query strategy to efficiently compute and validate daily distinct user counts (DAU) and weekly active users (WAU) for a system with ~200M users and ~5B events per month. Discuss partitioning, clustering, pre-aggregations/materialized views, approximation techniques (e.g., HLL), and validation checks to ensure counts are accurate within acceptable bounds.
MediumTechnical
0 practiced
Given a transactions table with columns (transaction_id, user_id, amount_cents, currency, processed_at), write a SQL query to find days where daily_sum(amount_cents) deviates from its 30-day moving average by more than 4 standard deviations, grouped by currency. Return date, currency, daily_sum, moving_avg, moving_std, and z_score. Describe potential pitfalls of this approach.
HardSystem Design
0 practiced
Design a production-grade metric validation and monitoring platform for a global BI team that manages ~10,000 metrics. Requirements: metric definitions and versioning, lineage, automated checks, anomaly detection, SLA monitoring, and alerting. Describe the architecture (metadata store, orchestration/scheduler, compute layer, storage), a data model for storing check results and lineage, and strategies to scale checks economically (sampling, incremental, caching).
MediumTechnical
0 practiced
Explain strategies for handling late-arriving data for analytics (events that belong to a prior processing window). Compare watermarking, holding windows and reprocessing, incremental backfills, and flagging late data in metrics. For each approach, describe trade-offs in complexity, cost, and data freshness.
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