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
Deep-dive: discuss how different consistency models (strong consistency, read-your-writes, eventual consistency, monotonic reads) affect the correctness and perception of aggregated BI metrics. For each model, give a concrete example scenario where it could mislead users and propose mitigation approaches (UI annotations, reconciliation windows, read-model choices) that a BI team can implement.
EasyTechnical
0 practiced
Explain eventual consistency in the context of dashboards and aggregated business metrics. Provide concrete examples of how eventual consistency might produce confusing or incorrect dashboard results for users, how you would detect those issues operationally, and at least three mitigation strategies a BI team can apply without changing the underlying data store.
HardSystem Design
0 practiced
Design a cost-aware autoscaling strategy and capacity plan for interactive dashboard services with highly spiky traffic (e.g., product launches or earnings reports). Include predictive scaling, reserved capacity for critical customers, warm-up strategies for caches and precomputations, throttling policies, and monitoring and cost-alerting mechanisms to prevent runaway spend during spikes.
HardSystem Design
0 practiced
Design a hybrid serving layer that supports both OLAP-style historical aggregations and near-real-time OLTP updates for interactive dashboards. Discuss architecture components (write path, OLAP store, serving layer), how to merge recent updates with pre-aggregated historical data, cache invalidation approaches, read/write separation, transactionality and consistency considerations, and strategies to keep end-to-end latency acceptable for interactive users.
EasyTechnical
0 practiced
Explain what a semantic layer (also called metrics or business logic layer) is in BI architecture and why it matters. Include responsibilities of the semantic layer, how it enforces consistent metric definitions across dashboards, how it interacts with visualization tools (Looker/Mode/Power BI), and governance practices you'd put in place to prevent metric drift across teams.
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