Covers the principles, frameworks, practices, and tooling used to ensure data is accurate, complete, timely, and trustworthy across systems and pipelines. Key areas include data quality checks and monitoring such as nullness and type checks, freshness and timeliness validation, referential integrity, deduplication, outlier detection, reconciliation, and automated alerting. Includes design of service level agreements for data freshness and accuracy, data lineage and impact analysis, metadata and catalog management, data classification, access controls, and compliance policies. Encompasses operational reliability of data systems including failure handling, recovery time objectives, backup and disaster recovery strategies, observability and incident response for data anomalies. Also covers domain and system specific considerations such as customer relationship management and sales systems: common causes of data problems, prevention strategies like input validation rules, canonicalization, deduplication and training, and business impact on forecasting and operations. Candidates may be evaluated on designing end to end data quality programs, selecting metrics and tooling, defining roles and stewardship, and implementing automated pipelines and governance controls.
HardSystem Design
65 practiced
Design governance and a technical architecture to manage a 'golden record' of customer across CRM, billing, and support systems. The solution must handle eventual consistency, conflict resolution (business rules, last-write-wins, and manual overrides), CDC-based synchronization, and an audit trail. Provide a high-level architecture, synchronization patterns (push vs. pull, event sourcing), and conflict-resolution policies with examples.
EasyBehavioral
44 practiced
In the Revenue Operations function, data stewards are key. Describe the responsibilities and daily activities of a data steward for a CRM contact dataset. Explain how you would identify and appoint stewards across Sales, Marketing, and Customer Success, how they should collaborate with a central revenue ops team, and outline a 60-day onboarding plan that enforces accountability.
HardSystem Design
39 practiced
Architect an end-to-end data quality program for Revenue Operations at scale (multi-product company ingesting ~100M rows/day). The program should cover people/process/technology: policies, SLAs, data contracts, ownership, tooling, monitoring, incident management, and rollout plan. Provide a phased implementation (pilot, scale), success metrics (KPIs), and a short ROI justification for leadership.
MediumSystem Design
40 practiced
Design an observability and monitoring stack for data quality targeting revenue datasets. List the key dataset-level and pipeline-level metrics you would collect (e.g., row-count delta, null rate, uniqueness, value-distribution summaries, schema changes), describe how you'd detect distributional drift or silent failures, and outline sample dashboards and alerting rules for engineers vs. business stakeholders.
MediumTechnical
71 practiced
You have two tables: opportunities(opportunity_id, account_id, amount, close_date, source_system) and accounts(account_id, name, created_at). Write SQL that identifies orphaned opportunities (opportunities whose account_id does not exist in accounts), counts orphans by source_system, and suggests SQL-based remediation steps to either link or quarantine those opportunities.
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