Technical Priorities and Challenges Questions
Identify the team's current technical priorities, pain points, and technical roadmap including architecture, technical debt, platform and tooling constraints, and business intelligence or data infrastructure considerations. Candidates should be able to discuss the current data stack and workflows, trade offs between short term fixes and longer term redesigns, success criteria for technical initiatives in the first 90 days and first year, and how their technical experience and decisions would address the team constraints while aligning with product goals.
HardSystem Design
0 practiced
Design an architecture to serve interactive BI dashboards for a product with 100k monthly active users and up to 20k concurrent dashboard viewers. Requirements: p95 widget load <500ms when cached, <2s for live queries, support per-tenant access controls, and graceful degradation when backends are slow. Describe components, data flows, caching strategy, fault tolerance, and how you would test scalability.
EasyTechnical
0 practiced
You're joining a new BI team as a Business Intelligence Analyst. In your first meeting with engineering and product, list and explain the top 6 technical priorities you would surface for the BI function (architecture, dashboard performance, data freshness, tooling, technical debt, observability). For each priority, explain the short-term business impact and what evidence or signals you'd use to validate the priority.
HardTechnical
0 practiced
Define a set of instrumentation metrics to prioritize technical debt in a BI service layer. Include both system metrics (p95 latency, error rate, incident frequency) and business-facing metrics (number of impacted dashboards, query failure impact). Show how to convert these into a prioritization score and an actionable backlog.
EasyTechnical
0 practiced
Explain circuit breakers and bulkheads and provide a concrete example of how each pattern would protect a BI aggregation service that calls multiple upstream microservices for data. Explain when to open breakers and how to partition resources with bulkheads.
HardTechnical
0 practiced
Design a canary or blue-green deployment strategy for a BI query microservice that must maintain correct rolling aggregates across versions with zero data loss and no downtime. Discuss how you would route traffic, validate results, and roll back safely if aggregates diverge.
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