资深数据工程师
Staff Data Engineer
在 ClickUp，我们正在打造未来的工作方式：首个真正融合 AI 的工作空间，将任务、文档、聊天、日历和企业搜索统一起来，所有功能都由以情境为中心的 AI 驱动。我们是一家以 AI 为核心的企业。每位团队成员都需要每天使用 AI，我们在招聘过程中会评估 AI 的熟练程度。加入我们，一起重新定义可能的边界。🚀
我们正在寻找一位资深数据工程师，负责我们数据平台的架构和技术愿景。这是一个高杠杆、高自主性的职位，你将设定团队的技术标准，推动跨职能的数据基础设施战略对齐，并解决我们最困难的工程问题。你将使用 AWS 无服务器技术、Snowflake、dbt 和 Terraform，但你的影响远不止于任何单一工具：你将塑造我们在平台层面如何思考可靠性、可扩展性、成本和开发者体验。
这个职位适合那些不仅构建优秀系统，还能让周围工程师变得更好的人。
职位职责：
- 负责 ClickUp 数据平台的技术架构，做出平衡可扩展性、成本、可靠性和速度的设计决策。
- 与领导层合作，定义并推动数据基础设施的技术路线图。
- 设计大规模系统：构建其他工程师日常使用的框架、抽象和模式。
- 领导涉及数据工程、分析工程、数据科学和数据分析的复杂跨团队技术项目。
- 推动云基础设施和计算的成本优化，将效率转化为竞争优势。
- 使用 AWS 无服务器（Lambda、Fargate、Step Functions、Kinesis、S3、DynamoDB、Aurora）、Snowflake 和 dbt 构建和演进我们的数据管道。
- 建立并推广工程标准：可观测性、测试、CI/CD、代码审查和文档实践。
- 设计并维护 AI/ML 工作负载的基础设施，包括 LLM 框架、特征管道、训练数据系统和模型监控。
- 指导高级工程师，通过设计评审提供技术指导，并提升团队的整体工程质量。
- 影响全公司范围内的技术决策，并在公司级架构讨论中代表数据工程。
任职要求：
- 在数据工程或后端/基础设施工程领域有显著的专业经验，至少有 3 年在高级或资深职位上的运营经验。
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At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀
We're looking for a Staff Data Engineer to own the architecture and technical vision of our data platform. This is a high-leverage, high-autonomy role where you'll set the technical bar for the team, drive cross-functional alignment on data infrastructure strategy, and solve our hardest engineering problems. You'll operate across AWS serverless technologies, Snowflake, dbt, and Terraform, but your impact goes well beyond any single tool: you'll shape how we think about reliability, scalability, cost, and developer experience at the platform level.
This role is for someone who doesn't just build great systems, but makes the engineers around them better.
The Role:
- Own the technical architecture of ClickUp's data platform, making design decisions that balance scalability, cost, reliability, and velocity.
- Define and drive the technical roadmap for data infrastructure in partnership with leadership.
- Design systems at scale: build frameworks, abstractions, and patterns that other engineers use daily.
- Lead complex, cross-team technical initiatives spanning data engineering, analytics engineering, data science, and data analytics.
- Drive cost optimization across cloud infrastructure and compute, turning efficiency into a competitive advantage.
- Build and evolve our data pipelines using AWS serverless (Lambda, Fargate, Step Functions, Kinesis, S3, DynamoDB, Aurora), Snowflake, and dbt.
- Establish and champion engineering standards: observability, testing, CI/CD, code review, and documentation practices.
- Design and maintain infrastructure for AI/ML workloads, including LLM frameworks, feature pipelines, training data systems, and model monitoring.
- Mentor senior engineers, provide technical guidance through design reviews, and raise the overall engineering quality of the team.
- Influence org-wide technical decisions and represent data engineering in company-level architecture discussions.
Qualifications:
- Significant professional experience in data engineering or backend/infrastructure engineering, with at least 3 years operating at a senior or staff level.
- Proven track record of owning architecture for data platforms or large-scale distributed systems.
- Deep expertise in AWS cloud services (Lambda, Fargate, Step Functions, S3, Kinesis, DynamoDB, Aurora) and infrastructure as code (Terraform and/or CDK).
- Expert-level SQL and Snowflake (or equivalent cloud data warehouse) knowledge, including performance tuning and cost optimization.
- Strong experience with dbt and modern ELT/ETL patterns at scale.
- Advanced Python skills with emphasis on building reusable libraries, frameworks, and tooling.
- Hands-on experience with orchestration frameworks (Airflow, Dagster, or Prefect) in production environments.
- Experience building data infrastructure for AI/ML: feature stores, training pipelines, embedding pipelines, model serving, or LLM integration.
- Deep understanding of streaming and event-driven architectures (Kinesis, Kafka, or equivalent).
- Mastery of CI/CD, Git workflows, containerization (Docker), and deployment automation.
- Strong communication skills: ability to write technical RFCs, influence without authority, and translate complex trade-offs for non-technical stakeholders.
- Track record of mentoring and growing engineers, with a multiplier mindset.
Desirable
- Experience operating data platforms at high scale (petabyte-level warehouses, millions of events/sec).
- Familiarity with data mesh or data product paradigms.
- Experience with FinOps practices and cloud cost management at scale.
- Prior experience in a technical leadership role without direct reports (staff/principal IC track).
- Contributions to open-source data tools or technical communities.
Equal Opportunity Employer
ClickUp is an Equal Opportunity Employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
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Visa Sponsorship
Please note we are unable to sponsor or take over sponsorship of an employment visa for roles outside of engineering and product at this time. Sponsorship for engineering and product roles is not guaranteed, but is instead based on the business needs for that specific role at that time. Please reach out to the recruiter with any questions.
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