Staff Data Engineer

Role overview

Join Dropbox as a Staff Data Engineer on the Analytics Data Engineering (ADE) team. You’ll tackle cross-cutting data challenges by standardizing analytics pipeline development and modernizing the analytics platform, partnering across Data Engineering, Data Science, Data Infrastructure, and Product Engineering.

Responsibilities

• Design and implement shared, reusable data models across multiple business domains
• Define conformed facts and dimensions (dimensional modeling, schema design)
• Establish and drive data engineering standards across ADE and functional analytics teams (including pipeline and CI/CD practices)
• Partner with data infrastructure to modernize orchestration, improve pipeline decomposition, and create secure dev/test environments
• Influence data-driven product and business decisions through reusable analytics engineering patterns

Requirements

• 8+ years of Python development, including building and maintaining production data pipelines
• 12+ years in data engineering / analytics engineering with increasing scope and technical leadership
• Deep expertise in dimensional data modeling, schema design, and scalable data architecture
• Proven ability to lead cross-team alignment: establish standards, influence without authority
• Strong orchestration and analytics engineering tooling:
  • Airflow strongly preferred
  • dbt for pipeline design, scheduling strategies, and failure recovery patterns
• 12+ years SQL with complex analytical queries, window functions, and performance optimization at scale (including Spark SQL)
• Experience with Databricks and modern lakehouse components (Unity Catalog, Delta Lake)

Nice-to-haves

• Familiarity with data governance and observability tools (e.g., Atlan, Monte Carlo, Great Expectations)
• Experience building or contributing to a metrics/semantic layer (e.g., dbt MetricFlow, Databricks Metric Views)
• Track record of establishing standards and best practices in a federated analytics organization