Original listing text, shown exactly as published by the company.
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
Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.
Requirements
- 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
Preferred Qualifications
- 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)…
