Role overview
dropbox is hiring for the Senior Manager, Data Engineering role in Remote - US: Select locations, US. It is a position with remote work available, Senior level, in the Tech sector. It was posted today.
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- Role
- Senior Manager, Data Engineering
- Company
- dropbox
- Location
- Remote - US: Select locations, US
- Work mode
- Remote
- Seniority
- Senior
- Sector
- Tech
- Posted
- today
Description
<h2>Role Description</h2>
<div>
<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">We are seeking a Senior Manager, Data Engineering to lead the team responsible for the reliability, quality, cost, and velocity of Dropbox's core data platform. This is a hands-on engineering leader who owns the pipelines and data products that Product, GTM, Finance, and the CTO organization depend on to make decisions.</span></div>
<div> </div>
<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">In this role, you will lead and grow a team of data engineers building and operating our ingestion, transformation, orchestration, and serving layers, as well as the self-serve analytics substrate that lets partner teams answer their own questions without bespoke engineering work.</span></div>
<div> </div>
<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">The ideal candidate is a deeply technical, product-minded engineering leader who can hold a high bar on system reliability and data quality while partnering closely with Data Science, Business Intelligence Engineering, Analytics, and Product to turn fragmented, ticket-driven data work into durable, reusable data products.</span></div>
</div>
<h2>Responsibilities</h2>
<ul class="listtype-bullet listindent1 list-bullet1" data-testid="bullet-list" data-test-indentation="1">
<li>
<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Data Quality & Observability: Establish and enforce a rigorous data quality culture: lineage, freshness monitoring, anomaly detection, and outcome-oriented, gaming-resistant quality metrics.</span></div>
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<li>
<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Self-Serve Platform: Lead the engineering of the self-serve analytics substrate, reducing bespoke request volume and increasing partner-team autonomy.</span></div>
</li>
<li>
<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Cost & Efficiency: Own the unit economics of the data platform — compute and storage efficiency — and drive measurable improvements without sacrificing reliability.</span></div>
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<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Cross-Functional Partnership: Partner deeply with Data Science, BIE, Analytics, Product, Data Platform, and the CTO org to define the semantic layer, modeling standards, and data contracts that make downstream work trustworthy and fast.</span></div>
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<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Engineering Culture: Establish rigorous engineering practices — code review, testing, CI/CD for data, incident response, and postmortems — and champion the effective, measured use of AI coding tools to improve engineering productivity.</span></div>
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<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Team Leadership: Lead, mentor, and grow a high-talent-density team of data engineers, fostering a culture of ownership, technical excellence, psychological safety, and continuous learning.</span></div>
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</ul>
<h2>Requirements</h2>
<ul class="listtype-bullet listindent1 list-bullet1" data-testid="bullet-list" data-test-indentation="1">
<li>
<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">8+ years of data engineering or backend/data infrastructure experience with increasing scope, ideally in high-scale environments.</span></div>
</li>
<li>
<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">3+ years of experience directly managing and growing engineering teams, including hiring, coaching, performance management, and team design.</span></div>
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<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Deep Technical Expertise: Proven track record building and operating large-scale batch and streaming pipelines</span> <span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze h-lparen">(e.g.,</span><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze"> Spark, dbt, Airflow/orchestration) on a modern lakehouse or warehouse stack</span> <span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze h-lparen">(e.g.,</span><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze"> Databricks, Snowflake, BigQuery).</span></div>
</li>
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<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Reliability & Quality: Demonstrated ownership of data SLAs, observability, lineage, and incident response for business-critical pipelines.</span></div>
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<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Systems & Modeling: Strong data modeling fundamentals and the ability to design a semantic layer and data contracts that serve many downstream consumers.</span></div>
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<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Stakeholder Management: Excellent communication and the ability to align engineering, data science, analytics, and business partners around shared reliability and quality goals.</span></div>
</li>
</ul>
<h2>Preferred Qualifications</h2>
<ul>
<li>
<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">Platform / Self-Serve Experience: Track record building self-serve data or analytics platforms that reduced bespoke request volume and increased partner autonomy.</span></div>
</li>
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<div><span class=" author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotuz85zgnz90zacz87zz122zgvz72zz86z85z67z5pz70zloy4qz74zz67zz73zz66znz70ze">AI-Forward Engineering: Experience integrating AI coding tools and LLM-based tooling into the engineering workflow, with a measured approach to imp
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