高级数据后端工程师
Senior Data-Backend Engineer
合同类型：B2B
工作方式：全远程
我们正在寻找一名数据后端工程师，加入分析工作区平台。
职责：
加速分析工程生命周期，最大化工程师的部署速度、操作可见性和财务控制。
我们负责数据平台中的分析工作区组件：Databricks 和 Power BI。我们提供工具、自动化和约束机制，让分析工程师快速行动而不破坏系统或超支。我们构建笔记本和报告的 CI/CD 流水线、成本监控仪表盘、工作区配置自动化以及自助模板，让正确的事情变得简单。
我们不只是管理工作区。我们将其视为一个平台产品，需要强大的后端工程来实现这一目标。
你将负责：
- 设计、构建并演进分析工作区平台的 API：分析工程师通过该程序化接口进行工作区、集群和报告的配置、发现和治理
- 架构并实现模块化单体后端，协调 Databricks API、Power BI REST API 和 Azure 基础设施 —— 弹性、可观测且易于扩展
- 构建和维护分析制品（笔记本、仪表盘、语义模型）的 CI/CD 流水线，使从开发到生产的发布自动化、可测试且可审计
- 设计并实现自动化工作流：按团队/项目归因成本、预算警报、自动扩展策略、空闲资源检测和合规约束
- 采集并展示操作可见性：部署指标、运行历史趋势、失败率和所有分析工作负载的 SLA 遵循情况
- 以代码形式管理工作区治理模型：访问控制、环境隔离、密钥管理和数据源连接策略，通过自动化而非手动步骤执行
构建自助工具和模板，减少分析工程师首次查询和首次仪表盘的时间
我们寻找的人选需具备：
- 扎实的软件工程背景：API 设计、模块化单体架构、测试、CI/CD 和容器化
- 在大规模生产 API 和后端自动化方面有实际经验
- 在 Microsoft Azure 上有深入的云架构经验（计算、网络、身份、托管服务、基础设施即代码）
- 熟练使用 Python（我们的主要自动化语言）
查看英文原文
Contract Type: B2B
Way of working: Full-Remote
We are looking for a Data-Backend Engineer for Analytics Workspaces Platform.
Mission:
Accelerate the analytics engineering lifecycle and maximize engineers' deployment velocity, operational visibility, and financial control.
We are a team own the analytics workspace components of the data platform: Databricks and Power BI. We provide the tooling, automation, and guardrails that let analytics engineers move fast without breaking things or blowing the budget. We build CI/CD pipelines for notebooks and reports, cost monitoring dashboards, workspace provisioning automation, and self-service templates that make the right thing the easy thing.
We don't just administer workspaces. We treat them as a platform product, and we needs trong backend engineering to make that real.
What you'll do:
- Design, build, and evolve the analytics workspace platform APIs: the programmatic interface through which analytics engineers provision, configure, discover, and govern their workspaces, clusters, and reports
- Architect and implement a modular monolith backend that orchestrates across Databricks APIs, Power BI REST APIs, and Azure infrastructure — resilient, observable, and easy to extend
- Build and maintain CI/CD pipelines for analytics artifacts — notebooks, dashboards, semantic models — so that promoting from dev to production is automated, testable, and auditable
- Design and implement automation workflows: cost attribution per team/project, budget alerts, auto-scaling policies, idle resource detection, and compliance guardrails
- Instrument and surface operational visibility: deployment metrics, run history trends, failure rates, and SLA adherence across all analytics workloads
- Own the workspace governance model as code: access control, environment separation, secret management, and data source connection policies enforced through automation, not manual steps
- Build self-service tooling and templates that reduce time-to-first-query and time-to-first dashboard for analytics engineers
What we're looking for
Must have:
- Strong software engineering background: API design, modular monolith architecture, testing, CI/CD, and containerization
- Proven experience building and maintaining production APIs and backend automation at scale
- Deep cloud architecture experience on Microsoft Azure (compute, networking, identity, managed services, infrastructure-as-code)
- Proficiency in Python (our primary automation languages)
- Experience designing systems with clear bounded contexts, contract-driven APIs, and evolvable module boundaries
Preferred:
- Experience building developer platforms, internal tools, or self-service portals
- Experience working with infrastructure-as-code such as Terraform
- Familiarity with Databricks, Power BI, or comparable analytics platforms — not as a user, butunderstanding their API surfaces and operational models
- Experience with FinOps practices — cost allocation, chargeback models, budget enforcement
- Knowledge of event-driven architectures, workflow orchestration, or job scheduling at scale
Originally posted on Himalayas