Principal Software Engineer

October 14

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Dropbox

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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.

1001 - 5000 employees

Founded 2007

🏢 Enterprise

⚡ Productivity

📋 Description

• Build and foster a strong engineering culture focused on excellence, velocity, and innovation • Define the architecture and integration strategy for new initiatives, including both internally developed and acquired products • Conduct technical due diligence on potential M&A targets to assess scalability, quality, and strategic fit • Rapidly prototype new product ideas and validate emerging opportunities that fall outside existing teams • Partner cross-functionally with product, design, and strategy teams to translate vision into technical execution • Maintain a high technical bar and guide the team through complex architectural decisions • Contribute to shaping Dropbox’s long-term innovation roadmap through technical insight and experimentation

🎯 Requirements

• 10+ years of full-stack engineering experience, including 3+ years in a technical leadership role • Proven success operating at both startup and enterprise scale—building early MVPs and scaling to millions of users • Deep understanding of product architecture evolution and trade-offs between speed and scalability • Experience with technical diligence and evaluating software systems for integration or acquisition • Some experience in engineering management preferred • Strong bias toward experimentation and comfort navigating high-ambiguity problem spaces