高级数据平台工程师
Senior Data Platform Engineer
关于产品与服务
产品与服务是一家产品设计和工程公司。
我们为世界上一些最大的企业解决关键任务挑战，拥有在高度监管行业（包括生命科学和金融服务）中的深厚专业知识。我们的以设计为导向的方法使我们能够将人工智能、数据和硬件工程的前沿能力应用于任何规模的企业。
总部位于美国，我们在墨西哥和英国设有区域开发中心。这一全球布局——以我们近岸模式为基础——使我们能够以客户期望的速度、效率和文化一致性进行大规模交付。
职位简介
产品与服务正在寻找一名高级数据平台工程师，负责现代湖仓平台的实施、维护和操作支持，重点在于 Microsoft Fabric / OneLake。
在此职位中，您将参与从需求到部署和生产支持的整个生命周期，将业务需求转化为可靠的数据产品，同时确保数据质量、监控和性能。您将与解决方案架构师紧密合作，确保在多个项目中遵循平台标准。
您将负责：
- 通过实施数据质量检查、监控异常、对管道故障进行根本原因分析，并主动识别和解决影响可靠性、成本或利益相关者信任的数据或流程问题，确保数据质量和可靠性。
- 实施与运维：设计、构建和维护数据摄取和编排组件（例如 Fabric 数据管道、笔记本），用于文件下传、API 和数据库提取等模式。
- 可靠性与质量：通过实施质量检查、监控异常并分析管道故障的根本原因，确保数据完整性。
- 架构与建模：开发符合定义粒度和键的定制转换层（银层/金层），以发布可靠的数据产品。
- 运营卓越：运营和监控平台组件（计划、警报、运行手册），并维护准确的“已构建”技术文档。
- 利益相关者协作：与分析/BI 团队合作，确保数据集支持报告需求，并协助验证和采用。
- 您需要具备：
- 5 年以上商业智能、数据仓库或湖仓项目的经验。
- 精通 Python 或基于 Spark 的转换和存储
查看英文原文
About Goods & Services
Goods & Services is a product design and engineering company.
We solve mission-critical challenges for some of the world’s largest enterprises, with deep expertise in highly regulated industries—including life sciences and financial services. Our design-led approach allows us to apply cutting-edge capabilities in AI,Data and Hardware Engineering to companies of any size.
Headquartered in the United States, we operate regional development centers in Mexico and the United Kingdom. This global footprint—anchored by our nearshore model—enables us to deliver at scale with the speed, efficiency, and cultural alignment our clients expect.
About the job
Goods & Services is looking for a Senior Data Platform Engineer who is responsible for hands-on implementation, maintenance, and operational support of modern lakehouse platforms, with a focus on Microsoft Fabric / OneLake.
In this role, you will participate in the full lifecycle from requirements to deployment and production support, translating business needs into reliable data products while ensuring data quality, monitoring, and performance. You will work closely with Solution Architects to follow platform standards across multiple engagements.
What you’ll do:
- Ensure data quality and reliability by implementing data quality checks, monitoring anomalies, performing root cause analysis on pipeline failures, and proactively identifying and resolving data or process issues impacting reliability, cost, or stakeholder trust.
- Implementation & Operations: Design, build, and maintain data ingestion and orchestration components (e.g., Fabric Data Pipelines, notebooks) for patterns such as file drops, APIs, and database extracts.
- Reliability & Quality: Ensure data integrity by implementing quality checks, monitoring anomalies, and performing root cause analysis on pipeline failures.
- Architecture & Modeling: Develop curated transformation layers (Silver/Gold) aligned to defined grains and keys to publish reliable data products.
- Operational Excellence: Operate and monitor platform components (schedules, alerts, runbooks) and maintain accurate “as-built” technical documentation.
- Stakeholder Collaboration: Work with analytics/BI teams to ensure datasets support reporting needs and assist with validation and adoption.
What you’ll need:
- 5+ years’ years in Business Intelligence, Data Warehouse, or Lakehouse projects.
- Proficiency in Python or Spark-based transformations and strong SQL skills for complex query analysis.
- Experience with Microsoft Fabric (preferred) or similar cloud data environments and orchestration tools.
- Deep understanding of Delta table concepts (partitioning, schema evolution) and dimensional data modeling (grain/keys).
- Proven ability to manage the full development lifecycle and troubleshoot performance problems across ingestion and consumption layers.
- Ability to express complex technical concepts in business terms and work effectively in a team environment.
Nice to have:
- Familiarity with reporting/semantic consumption patterns (Power BI familiarity is a plus).
- Comfort working in a client-services environment (multiple engagements, shifting priorities, clear communication, strong documentation).
- Knowledge of multi-unit retail/restaurant analytics is helpful but not required.
Originally posted on Himalayas