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Project Deep Dives and Technical Decisions Questions

Detailed personal walkthroughs of real projects the candidate designed, built, or contributed to, with an emphasis on the technical decisions they made or influenced. Candidates should be prepared to describe the problem statement, business and technical requirements, constraints, stakeholder expectations, success criteria, and their specific role and ownership. The explanation should cover system architecture and component choices, technology and service selection and rationale, data models and data flows, deployment and operational approach, and how scalability, reliability, security, cost, and performance concerns were addressed. Candidates should also explain alternatives considered, trade off analysis, debugging and mitigation steps taken, testing and validation approaches, collaboration with stakeholders and team members, measurable outcomes and impact, and lessons learned or improvements they would make in hindsight. Interviewers use these narratives to assess depth of ownership, end to end technical competence, decision making under constraints, trade off reasoning, and the ability to communicate complex technical narratives clearly and concisely.

HardTechnical
0 practiced
You're building an experimentation (feature-flag) analytics pipeline. Explain how to ensure that experimentation bucketing, user identity, and instrumentation produce correct, unbiased experiment metrics across distributed services. Include rollback and replay considerations.
EasyTechnical
0 practiced
Design a simple RESTful analytics API contract to serve time-series metrics for a dashboard. Specify endpoint(s), parameters (time range, granularity, filters), expected response format (example JSON), error handling behavior, and rate-limiting considerations. Explain why your choices make the API easy for dashboard developers to consume and robust under load.
HardTechnical
0 practiced
Describe how you would lead a cross-functional decision about which events to instrument for a newly planned product feature. Cover stakeholder alignment, cost-benefit evaluation, minimal viable instrumentation, telemetry ownership, and how you'd ensure analytics needs are met without over-instrumenting.
MediumTechnical
0 practiced
You discover inconsistent metric definitions across teams (e.g., different definitions of 'active user'). As an analytics owner, outline the process to standardize metric definitions, enforce them, and migrate existing dashboards to the canonical definition while minimizing disruption.
HardSystem Design
0 practiced
You are building a dashboard for an internal executive team that requires provenance for every KPI. Explain how you would implement provenance tracking end-to-end (from event to dashboard), what metadata you would store, and how you would enable 'replay' or 'audit' of a KPI calculation on demand.

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