Data Quality and Edge Case Handling Questions

Practical skills and best practices for recognizing, preventing, and resolving real world data quality problems and edge cases in queries, analyses, and production data pipelines. Core areas include handling missing and null values, empty and single row result sets, duplicate records and deduplication strategies, outliers and distributional assumptions, data type mismatches and inconsistent formatting, canonicalization and normalization of identifiers and addresses, time zone and daylight saving time handling, null propagation in joins, and guarding against division by zero and other runtime anomalies. It also covers merging partial or inconsistent records from multiple sources, attribution and aggregation edge cases, group by and window function corner cases, performance and correctness trade offs at scale, designing robust queries and pipeline validations, implementing sanity checks and test datasets, and documenting data limitations and assumptions. At senior levels this expands to proactively designing automated data quality checks, monitoring and alerting for anomalies, defining remediation workflows, communicating trade offs to stakeholders, and balancing engineering effort against business risk.

HardSystem Design

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Design an end-to-end test harness that can validate pipeline changes against production-like data without writing to production datasets. Include strategies for data masking, selecting representative subsets that preserve distribution, running parallel shadow/canary pipelines, diffing outputs while tolerating nondeterminism, and infrastructure and safety measures to prevent accidental write-through.

EasyTechnical

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As an SRE, describe three safe rollout strategies for evolving a schema that multiple downstream consumers rely on (for example: add nullable columns, topic versioning, consumer contract testing). For each strategy explain how you would detect consumer breakage, what monitoring or CI gates are required, and how to roll back safely.

MediumTechnical

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Explain how SQL GROUP BY behaves when grouping on columns that can contain NULLs and when joining facts to dimension tables can produce missing groups. Provide sample SQL that ensures a row exists per expected dimension (zero-filled metrics) even if no facts exist. Discuss the performance implications for large dimension tables and indexing considerations.

HardSystem Design

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Architect a cross-region reconciliation system that detects and resolves divergent aggregates (for example, daily totals) that are produced independently in two regions under eventual consistency. Describe the reconciliation algorithm, methods to guarantee idempotency of repairs, tolerances to accept small divergence, how to notify downstream consumers, and how to perform repair with minimal consumer impact.

MediumTechnical

0 practiced

Describe how to automate anomaly detection for key pipeline metrics (ingest rate, schema changes, null-rate) using statistical methods or light-weight ML. Cover feature extraction, method selection (stat tests vs simple models), runtime constraints, false-positive management, and the runbook actions SREs should take when anomalies are detected.

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