Dropbox
Staff
Architect the shared data models and metrics layer that make Dropbox's analytics trustworthy
Dropbox's Analytics Data Engineering team, inside Data Science & AI Platform, is modernizing the analytics platform end to end — new orchestration, shared conformed-dimension data models, a certified metrics framework, and shift-left governance. This Staff role leads the design of reusable data models and semantic/metrics layers, drives cross-team standardization, and gets direct exposure to senior leadership shaping the technical direction.
走进这场面试
免费 · 一场按这个岗位校准的真语音模拟
这场面试考什么
- Dimensional data modeling: shared fact tables, conformed dimensions, semantic/metrics layer
- Cross-team standardization and influence without direct authority
- Shift-left data governance: contracts, SLOs, code-enforced quality gates
- Orchestration (Airflow) and dbt pipeline design, scheduling, failure recovery
- Advanced SQL and Spark SQL performance at scale
- Reducing operational burden: observability, on-call sustainability, runbooks
常见提问方向
Design a certified metrics framework that multiple business lines can trust as the single source of truth
A conflict over a metric definition between Data Science and BI — how you resolved it
Building a data contract with an upstream producer to catch quality issues before production
Modernizing orchestration or migrating pipeline patterns at scale — what broke and how you recovered
Where AI-assisted pipeline development or conversational data exploration genuinely helps vs. hype
Driving adoption of a new data modeling standard across a federated analytics org
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