高级经理，数据工程
Senior Manager, Data Engineering
职位描述
我们正在寻找一位高级数据工程经理，领导负责Dropbox整体数据基础架构的团队。这是一位亲自动手的工程领导者，负责产品、GTM、财务和CTO部门依赖的数据管道和数据产品，以做出决策。
在这个职位上，你将带领并发展一支数据工程师团队，构建和运营我们的数据采集、转换、编排和交付层，以及让合作伙伴团队无需定制工程工作即可自行回答问题的自助分析基础架构。
理想的候选人是一位技术深厚、注重产品的工程领导者，能够在与数据科学、商业智能工程、分析和产品团队紧密合作的同时，保持系统可靠性和数据质量的高标准，将零散的、基于工单的数据工作转化为持久且可重用的数据产品。
我们的工程职业发展框架对所有公司外部人员开放，描述了我们在各个职业级别对工程师的期望。请查看我们关于此主题和其他内容的博客文章。
职责
- 数据质量与可观测性：建立并执行严格的数据质量文化：数据血缘、新鲜度监控、异常检测，以及以结果为导向、抗游戏化的质量指标。
- 自助平台：领导自助分析基础架构的工程开发，减少定制请求量，提高合作伙伴团队的自主性。
- 成本与效率：负责数据平台的单位经济——计算和存储效率，并在不牺牲可靠性的情况下推动可衡量的改进。
- 跨职能合作：与数据科学、BIE、分析、产品、数据平台和CTO团队深度合作，定义语义层、建模标准和数据契约，使下游工作更加可信和高效。
- 工程文化：建立严格的工程实践——代码审查、测试、数据CI/CD、事件响应和事后分析，并倡导有效、有依据地使用AI编码工具来提高工程生产力。
- 团队领导：领导、指导并发展一支高人才密度的数据工程师团队，营造一种拥有责任感、技术卓越、心理安全和持续学习的文化。
要求
- 8年以上数据工程或后端/数据基础设施经验，职责范围逐步扩大，最好是在高吞吐量、大规模数据处理环境中的经验。
查看英文原文
Role Description
We are seeking a Senior Manager, Data Engineering to lead the team responsible for Dropbox’s underlying data foundations that power our business as a whole. 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.
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.
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.
Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here.
Responsibilities
- Data Quality & Observability: Establish and enforce a rigorous data quality culture: lineage, freshness monitoring, anomaly detection, and outcome-oriented, gaming-resistant quality metrics.
- Self-Serve Platform: Lead the engineering of the self-serve analytics substrate, reducing bespoke request volume and increasing partner-team autonomy.
- Cost & Efficiency: Own the unit economics of the data platform — compute and storage efficiency — and drive measurable improvements without sacrificing reliability.
- 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.
- 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.
- 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.
Requirements
- 8+ years of data engineering or backend/data infrastructure experience with increasing scope, ideally in high-scale environments.
- 3+ years of experience directly managing and growing engineering teams, including hiring, coaching, performance management, and team design.
- Deep Technical Expertise: Proven track record building and operating large-scale batch and streaming pipelines (e.g., Spark, dbt, Airflow/orchestration) on a modern lakehouse or warehouse stack (e.g., Databricks, Snowflake, BigQuery).
- Reliability & Quality: Demonstrated ownership of data SLAs, observability, lineage, and incident response for business-critical pipelines.
- Systems & Modeling: Strong data modeling fundamentals and the ability to design a semantic layer and data contracts that serve many downstream consumers.
- Stakeholder Management: Excellent communication and the ability to align engineering, data science, analytics, and business partners around shared reliability and quality goals.
Preferred Qualifications
- Platform / Self-Serve Experience: Track record building self-serve data or analytics platforms that reduced bespoke request volume and increased partner autonomy.
- AI-Forward Engineering: Experience integrating AI coding tools and LLM-based tooling into the engineering workflow, with a measured approach to impact and guardrails.
- Cost Discipline: Demonstrated success improving compute/storage unit economics without regressing reliability.
- Familiarity with modern data governance, privacy, and access-control practices.
- Experience operating in a pod or embedded model serving multiple business partners.
Durable Skills
AI fluency means using these tools to amplify human judgment, not replace it. We believe people with these skills will thrive as work and technology continue to evolve:
- Awareness: Understand yourself and others.
- Judgment: Evaluate information and make decisions in complex situations.
- Adaptability: Learn, adjust, and stay effective through change.
- Connection: Communicate, collaborate, and build trust.
To learn more about why these skills matter and what the data shows about thriving through change, read this blog post from our Chief People Officer, Melanie Rosenwasser.
Compensation
Canada Pay Range
$209,100—$282,900 CAD