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工作空间平台高级工程经理

机器翻译
查看雇主原标题Senior Engineering Manager for Workspace Platform

Databricks · San Francisco, California

职位信息来自雇主公开的招聘页面。申请前请务必在雇主官网核实详情。

为什么值得关注?

发现指数 44/100,仅依据与该职位一起存储的证据计算。

44/100 发现指数
  • 新的雇主官方职位

分数构成

  • 时效性 (随职位发布时间变化)+18
  • 雇主官方来源+15
  • 稀有职位+3
  • 公司来源健康度+8

该职位未包含:已披露薪资、远程职位、提及签证担保、提及搬迁、未出现在监控的职位板上。

这些理由来自雇主自己的职位描述与我们核实过的来源检查结果。除了已存储的信号之外,我们不做任何推测。

职位描述

机器翻译

RDQ225R488

在 Databricks,我们热衷于帮助数据团队解决世界上最棘手的问题——从让下一代交通方式成为现实,到加速医学突破的发展。我们通过构建和运行世界上最好的数据与 AI 基础设施平台来实现这一目标,让我们的客户能够利用深度数据洞察来改善他们的业务。

Workspace Platform 团队肩负着一项雄心勃勃的使命:将我们的客户群扩大 100 倍,并支持未来 agentic AI 工作负载。我们的目标是构建一个统一、一致且基础性的共享平台,服务于整个 Databricks Workspace 体验。

作为 Workspace Platform 团队的高级工程经理,您将领导开发统一基础设施,为多个 Databricks 产品中关键的面向客户能力提供支持,包括内容发现(类似于 Google Search)、内容组织(类似于 Google Drive)、协作式代码编辑和代码仓库管理(类似于 Github)。这是一个高影响力且令人兴奋的机会,您将带领一支约 20 名软件工程师的团队,打造平台功能、直观的工作区体验以及合作伙伴集成,这些对 Databricks 的扩张和用户采用至关重要。

您将产生的影响:

• 战略与愿景:定义并推动 Workspace Platform 的技术战略,包括构建 16 项以上支撑整个 Databricks 体验的共享能力。

• 执行责任:负责平台路线图、执行和交付,确保所有团队交付成果达到最高的质量和及时性标准。

• 工程卓越:建立并执行团队在工程卓越方面的最佳实践,包括设计评审、代码质量、测试策略,以及面向高吞吐分布式系统的性能优化。

• 跨职能协作:与组织内各团队紧密合作,包括开发垂直产品体验的团队(Lakehouse Platform、SQL Warehouse、AI/BI Dashboards、UC Platform)以及核心基础设施团队,以确保无缝集成和创新。

我们寻找的人才:

• 经验:15 年以上软件工程经验,并拥有出色的技术领导力和影响力记录。

• 管理:5 年以上工程管理经验,包括管理其他经理的经验。

• 规模化:拥有将工程团队从 10 名扩展到 30 名以上工程师的成熟经验。

• 平台专长:拥有在 SaaS 环境中领导平台团队和后端开发人员的经验。

• 技术深度:

• 拥有设计和构建可扩展分布式系统的经验。

• 熟悉在容器化技术之上构建服务。

• 精通分布式系统、主流云平台(AWS、Azure、GCP)以及现代 Web 应用架构。

• 协作:具备强大的跨产品、工程和业务团队协作能力,使技术战略与公司核心增长目标保持一致。

• 热情:对构建和扩展稳健、高性能平台充满热情。

福利待遇

在 Databricks,我们致力于提供全面的福利和津贴,以满足所有员工的需求。如需了解您所在地区所提供福利的具体详情,请点击此处。

我们对多元与包容的承诺

在 Databricks,我们致力于营造多元且包容的文化,让每个人都能脱颖而出。我们非常重视确保我们的招聘实践具有包容性,并符合平等就业机会标准。在 Databricks 寻求就业机会的个人,不会因年龄、肤色、残疾、族裔、家庭或婚姻状况、性别认同或表达、语言、国籍、身体和心理能力、政治派别、种族、宗教、性取向、社会经济状况、退伍军人身份以及其他受保护特征而受到区别对待。

合规

如果履行工作职责需要接触受出口管制的技术或源代码,雇主可自行决定是否为此类职位申请美国政府许可证,且雇主可能仅基于这一原因而拒绝继续推进某位申请人。

薪资

Databricks 致力于公平和公正的薪酬实践。该职位的薪资范围列于下方,代表非佣金制职位的预期薪资范围或佣金制职位的目标收入。实际薪酬方案取决于每位候选人独有的多项因素,包括但不限于与工作相关的技能、经验深度、相关认证和培训,以及具体工作地点。基于上述因素,Databricks 预计将使用该薪资范围的全部区间。该职位的总薪酬方案还可能包括年度绩效奖金、股权以及上述福利。如需了解您所在地点属于哪个薪资范围,请访问我们的页面此处。

当地薪资范围 $217,000 — $312,200 USD

关于 Databricks

Databricks 是一家数据与 AI 公司。全球超过 20,000 家组织——包括 adidas、AT&T、Bayer、Block、Mastercard、Rivian、Unilever,以及 70% 的 Fortune 500 企业——依赖 Databricks Data + AI Platform 来构建和扩展数据与 AI 应用、分析和代理。Databricks 总部位于 San Francisco,在全球拥有 30 多个办事处,提供统一平台,包括 Genie、Lakebase、Agent Bricks、Lakeflow、Lakehouse 和 Unity Catalog。如需了解更多信息,请在 LinkedIn、X、YouTube 和 Instagram 上关注 Databricks。

以上内容由机器翻译自动生成,可能存在错误;投递前请以雇主原文为准。

查看雇主原文

职位描述

RDQ225R488

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.

The Workspace Platform team is on an ambitious mission to scale our customer base 100x and support the future of agentic AI workloads. Our goal is to build a unified, consistent, and foundational Shared Platform for the entire Databricks Workspace experience.

As a Senior Engineering Manager on the Workspace Platform team, you will lead the development of unified infrastructure powering critical customer-facing capabilities across multiple Databricks products, including Content Discovery (akin to Google Search), Content Organization (akin to Google Drive), Collaborative code editing, and Repository management (akin to Github). This is a high-impact, exciting opportunity to guide a team of ~20 software engineers in creating platform features, intuitive workspace experiences, and partner integrations that are essential to Databricks' expansion and user adoption.

The impact you will have:

• Strategy & Vision : Define and drive the technical strategy for the Workspace Platform, including building over 16 shared capabilities that underpin the entire Databricks experience.

• Execution Ownership : Own the roadmap, execution, and delivery for the platform, ensuring all team deliverables meet the highest standards of quality and timeliness.

• Engineering Excellence: Establish and enforce team best practices for engineering excellence, including design reviews, code quality, testing strategies, and performance optimizations for high-volume distributed systems.

• Cross-Functional Collaboration: Partner closely with teams across the organization, including those developing vertical product experiences (Lakehouse Platform, SQL Warehouse, AI/BI Dashboards, UC Platform) and core infrastructure, to ensure seamless integration and innovation.

What we look for:

• Experience: 15+ years of software engineering experience with a strong track record of technical leadership and impact.

• Management: 5+ years of engineering management experience, including experience managing other managers.

• Scaling: Proven experience scaling engineering teams from 10 to 30+ engineers.

• Platform Expertise: Experience leading platform teams and backend developers in a SaaS environment.

• Technical Depth:

• Experience designing and building scalable distributed systems.

• Familiarity with building services on top of containerization technologies.

• Expertise in distributed systems, major cloud platforms (AWS, Azure, GCP), and modern web application architectures.

• Collaboration: Strong ability to collaborate across product, engineering, and business teams to align technical strategy with core company growth objectives.

• Passion: A passion for building and scaling robust, high-performance platforms.

福利待遇

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here .

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

薪资

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here .

Local Pay Range $217,000 — $312,200 USD

About Databricks

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram .

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