Note: The job is a remote job and is open to candidates in USA. Dropbox is looking for a Staff Data Engineer to join their Analytics Data Engineering team within Data Science & AI Platform. The role involves solving cross-cutting data challenges, modernizing the analytics platform, and establishing analytics engineering standards while collaborating with various teams to influence data-driven decisions.
Responsibilities
- Lead the design and implementation of shared, reusable data models, defining shared fact tables, conformed dimensions, and a semantic/metrics layer that serves as the single source of truth across analytics functions
- Drive standardization of data engineering practices across ADE and functional analytics teams, including pipeline patterns, CI/CD workflows, naming conventions, and data modeling standards
- Partner with Data Infrastructure to modernize orchestration, improve pipeline decomposition, and establish secure dev/test environments with production data access
- Architect and implement a shift-left data governance strategy, working with upstream data producers to establish data contracts, SLOs, and code-enforced quality gates that catch issues before production
- Collaborate with Data Science leads and Product Management to translate metric definitions into reliable, certified data pipelines that power executive dashboards, WBR reporting, and growth measurement
- Reduce operational burden by improving pipeline granularity, observability, and failure recovery, establishing runbooks and alerting standards that make on-call sustainable
- Evaluate and integrate AI-native tooling into the data development lifecycle, enabling conversational data exploration with guardrails and AI-assisted pipeline development
Skills
- BS degree in Computer Science or related technical field, or equivalent technical experience
- 12+ years of experience in data engineering or analytics engineering with increasing scope and technical leadership
- 12+ years of SQL experience, including complex analytical queries, window functions, and performance optimization at scale (Spark SQL)
- 8+ years of Python development experience, including building and maintaining production data pipelines
- Deep expertise in dimensional data modeling, schema design, and scalable data architecture, with hands-on experience building shared data models across multiple business domains
- Strong experience with orchestration tools (Airflow strongly preferred) and dbt, including pipeline design, scheduling strategies, and failure recovery patterns
- Demonstrated ability to drive cross-team technical alignment, establishing standards, influencing without authority, and working across Data Engineering, Data Science, Data Infrastructure, and Product Engineering boundaries
- Experience with Databricks (Unity Catalog, Delta Lake) and modern lakehouse architectures
- Experience leading orchestration or platform modernization efforts at scale
- Familiarity with data governance and observability tools such as Atlan, Monte Carlo, Great Expectations, or similar
- Experience building or contributing to a metrics/semantic layer (dbt MetricFlow, Databricks Metric Views, or equivalent)
- Track record of establishing data engineering standards and best practices in a federated analytics organization
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