Staff Data Engineer, Analytics Data Engineering
Role Description Dropbox is looking for a Staff Data Engineer to join our Analytics Data Engineering (ADE) team within Data Science & AI Platform. You will be responsible for solving cross-cutting data challenges that multiple lines of business while driving standardization in how we build, deploy, and govern
What this role actually needs.
Role Description Dropbox is looking for a Staff Data Engineer to join our Analytics Data Engineering (ADE) team within Data Science & AI Platform. You will be responsible for solving cross-cutting data challenges that multiple lines of business while driving standardization in how we build, deploy, and govern 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 Requirements: - BS degree in Computer Science or related technical field, or equivalent technical experience - 1 2 + years of experience in data engineering or analytics engineering with increasing scope and technical leadership - 1 2 + 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 Company context: Dropbox is the public smart-workspace company building file collaboration, signature, and AI-assisted document tools.
Day-to-day expectations
Dropbox lists these responsibilities for the Staff Data Engineer, Analytics Data Engineering role.
- 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
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- BS degree in Computer Science or related technical field, or equivalent technical experience
- 1 2 + years of experience in data engineering or analytics engineering with increasing scope and technical leadership
- 1 2 + 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
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Source: jobs.dropbox.com · Source ID: 7595183 · Confidence: 88/100 · Last checked: May 7, 2026
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