Staff Data Engineer

🕒 March 26

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Dropbox

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2007

🏢 Enterprise

⚡ Productivity

Cloud Storage • Enterprise • Productivity

Dropbox is a cloud-based service that provides tools for storing, sharing, and accessing files across devices. It offers features such as document sharing, video review, automatic backups, and AI-driven scheduling. Dropbox also provides solutions for different sectors like teams, sales, marketing, and education, and industries including construction, media, technology, and manufacturing. With a focus on security, Dropbox ensures files are encrypted and protected against tampering. It offers integrations with various productivity tools and is trusted by major companies for efficient file management and collaboration.

📋 Description

• 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

🎯 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

🏖️ Benefits

• Health insurance • Retirement plans • Paid time off • Flexible work arrangements • Professional development