Learning From Failure and Continuous Improvement Questions
This topic focuses on how candidates reflect on mistakes, failed experiments, and suboptimal outcomes and convert those experiences into durable learning and process improvement. Interviewers evaluate ability to describe what went wrong, perform root cause analysis, execute immediate remediation and course correction, run blameless postmortems or retrospectives, and implement systemic changes such as new guardrails, tests, or documentation. The scope includes individual growth habits and team level practices for institutionalizing lessons, measuring the impact of changes, promoting psychological safety for experimentation, and mentoring others to apply learned improvements. Candidates should demonstrate humility, data driven diagnosis, iterative experimentation, and examples showing how failure led to measurable better outcomes at project or organizational scale.
HardTechnical
0 practiced
You have a 500M-row events table with columns (service, event_type, timestamp, request_id, payload). Describe a high-level plan and sample SQL/Python patterns to efficiently reconstruct timelines for a cascading failure affecting multiple services: group by request_id, order events, and identify the first error per request. Explain indexing, partitioning, handling missing or out-of-order events, and performance optimizations.
MediumTechnical
0 practiced
Case study: A weekly revenue dashboard shows a sudden drop because some transactions were assigned to the wrong day due to timezone misalignment between ingestion and reporting. As the BI Analyst, outline immediate mitigations for executives, steps for root-cause investigation, the structure of the incident postmortem, and systemic fixes (ETL changes, tests, docs) you would implement. Describe how you'd measure successful recovery and prevention.
MediumTechnical
0 practiced
Design an automated data validation test suite for BI pipelines that runs before production releases. Specify categories of tests (schema conformity, null/consistency checks, row-count and volume checks, referential integrity, distributional anomaly checks), where these should run (CI, staging), how failures should be surfaced, and how to handle flaky tests so they do not block critical deployments unnecessarily.
HardSystem Design
0 practiced
Design a centralized post-incident platform for an enterprise BI organization: searchable postmortems, RCA templates, automated ingestion of incident metrics (MTTD/MTTR), action-item tracking with owners and SLAs, and experiment tracking for fixes. Describe the data model, UI features, integrations (ticketing, Slack), and how you would measure adoption and business impact of the platform.
MediumTechnical
0 practiced
Given table transactions(transaction_id UUID, user_id UUID, amount DECIMAL, occurred_at TIMESTAMP), write an ANSI SQL (or Postgres) query that flags days where a user's daily total is an outlier defined as: daily_total > mean_daily_total_last_30_days + 3 * stddev_daily_total_last_30_days. Include handling for users with fewer than 5 prior days and explain your assumptions about windowing and performance.
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