Major Technical Decisions and Trade Offs Questions

Behavioral and leadership oriented topic asking candidates to present real examples of significant technical decisions they influenced. Candidates should prepare two to three concrete examples that cover the problem context, options considered, reasoning and evaluation of trade offs, stakeholder engagement and buy in, the final decision, implementation approach, measured outcomes, and retrospective lessons including what they would do differently. This topic assesses ownership, influence, communication, cross functional collaboration, and ability to defend and learn from organizational level technical choices.

HardTechnical

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You moved datasets to a columnar format and applied aggressive compaction which broke some consumer queries due to partition pruning differences. Describe how you managed compatibility, what feature flags or toggles you used, the reconciliation steps, and long-term policies to prevent similar breakages.

EasyTechnical

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Describe how you coached or mentored engineers who were stakeholders in a major technical decision. Give an example where you helped teammates understand trade-offs, write or review an RFC, and achieve consensus while developing skills in the process.

HardTechnical

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You want to apply an SRE approach to data pipelines: set SLOs, error budgets, on-call rotations, and runbooks. Describe how you'd define meaningful SLOs for data freshness, completeness, and correctness, how to map error budgets to runbook actions and product impact, and how to scale on-call without burning out engineers.

HardTechnical

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You need to design a data platform with privacy-by-design for sensitive PII data vs retrofitting privacy controls later. Explain trade-offs in cost, development time, business agility, and compliance risk. Recommend an approach for a growth-stage company and a plan to implement it.

MediumTechnical

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A legacy daily ETL job is taking many hours to complete and blocking analytics. Explain how you'd decide whether to rewrite it in Spark, optimize the SQL, or change to incremental loads. Include evaluation criteria, estimated ROI, risk assessment, and rollout approach.

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