Standards and Governance Questions

Evaluate the candidate ability to define, establish, and communicate standards and best practices that raise quality and consistency across teams. This includes creating standards for data quality, engineering practices, code review, security hygiene, testing, and documentation, as well as processes for adoption, enforcement, and continuous improvement. Candidates should discuss stakeholder engagement strategies, change management to shift culture without formal authority, mechanisms for measuring compliance and impact, and examples of standards they introduced or improved and the organizational outcomes.

HardTechnical

0 practiced

Build an incident-response runbook for data incidents that affect executive dashboards. Include detection methods, triage steps, communication templates for stakeholders, assigned roles (analyst, engineer, comms), SLAs for mitigation, and post-mortem requirements.

EasyTechnical

0 practiced

Describe recommended naming conventions for tables, views, and fields in a SQL analytics warehouse. Provide concrete examples for an orders table and a customer identifier field, and explain trade-offs (readability vs brevity).

MediumTechnical

0 practiced

How would you measure and report the business impact of implementing a set of data quality checks? Provide specific metrics to collect (e.g., incidents prevented, time saved), a sample calculation tying improved data quality to cost or revenue impact, and how to present this to stakeholders.

MediumTechnical

0 practiced

Create a data-retention policy for transactional analytics data that balances business needs, regulatory requirements, and storage costs. Describe retention windows by data type, anonymization/pseudonymization strategies for older records, and enforcement or archival mechanisms you would put in place.

HardSystem Design

0 practiced

Design a system to capture end-to-end data lineage capable of answering: "Which dashboards, alerts, and reports are impacted if table X changes?" Describe the data model (graph or DAG), storage choices, query patterns for impact analysis, integration with CI/CD, and how to tie owners to lineage nodes.

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