#136209 - 数据工程师 - 人力资源分析与AI就绪数据
#136209 - Data Engineer - HR Analytics & AI-Ready Data
简介
我们正在寻找一位经验丰富的数据工程师，负责构建和维护安全、可靠且可扩展的数据管道、数据仓库模型和分析数据集，以支持人员洞察。该职位结合了实际的数据工程工作与数据建模、平台可靠性、语义层设计以及对商业智能工具和人工智能接口的受控访问。
企业经验优先考虑。
主要职责
- 使用 dbt、PySpark 和 Python 应用程序开发和维护安全高效的数据管道。
- 使用 Python、dbt、Terraform、AWS Glue、Amazon EMR 和 Amazon S3 构建数据抽取、转换和加载基础设施。
- 从 API、云系统、Google 表格和其他结构化数据源集成数据。
- 创建和维护 Snowflake 数据仓库模型、数据集市和分析就绪的数据集。
- 开发自动化数据质量测试并改进内部数据工程流程。
- 监控生产数据管道并协助实现 99.5% 的正常运行时间目标。
- 在数据仓库模型上设计语义视图、本体层、面向业务的实体、关系和认证指标。
- 使用 Snowflake Cortex Analyst、Cortex Search 或等效的 LLM 原生查询层构建受控的自然语言数据体验。
- 配置受控数据源与内部 AI 工具之间的安全 Model Context Protocol 连接或类似接口。
- 完整地记录数据模型、数据管道、业务逻辑、操作流程和技术决策。
必备技能
- 5 年以上相关经验。
- 熟练使用 Snowflake，包括数据建模、数据集市和数据仓库设计。
- 熟练使用 dbt 进行数据转换。
- 熟练使用 Python，包括面向对象编程和数据脚本。
- 熟练使用 Airflow 进行数据管道编排。
- 具备集成 REST API 和从外部数据源获取数据的经验。
- 熟练使用 Google BigQuery 查询和优化。
- 具备大规模安全处理敏感数据的经验。
- 具备从 API、Google 表格或类似来源进行实时或近实时数据处理的经验。
- 强大的 SQL 技能，包括高度优化的查询。
- 全面的技术文档编写能力。
- 高级英语沟通能力。
加分技能
- 具备设计语义层或语义模型的经验，能够在 dbt 和数据仓库模型之上提供面向业务的抽象。
查看英文原文
We are seeking an experienced Data Engineer to build and maintain secure, reliable, and scalable data pipelines, warehouse models, and analytical datasets supporting people insights. This role combines hands-on data engineering with data modeling, platform reliability, semantic-layer design, and governed access for both business intelligence tools and AI-enabled interfaces.
The strongest candidates will demonstrate applied depth, not only familiarity with tools. They should be able to explain the architecture choices and tradeoffs behind the data platforms, pipelines, storage patterns, and AI-enabled capabilities they have personally built or supported.
Enterprise experience strongly preferred.
Key Responsibilities
- Develop and maintain secure, efficient data pipelines using dbt, PySpark, and Python applications.
- Build extraction, transformation, and loading infrastructure using Python, dbt, Terraform, AWS Glue, Amazon EMR, and Amazon S3.
- Integrate data from APIs, cloud systems, Google Sheets, and other structured sources.
- Create and maintain Snowflake warehouse models, datamarts, and analytics-ready datasets.
- Apply sound data-engineering fundamentals when designing storage, file-layout, and query-performance strategies for analytical workloads.
- Develop automated data-quality tests and improve internal data-engineering processes.
- Monitor production pipelines and help maintain a 99.5% uptime objective.
- Design semantic views, ontology layers, business-friendly entities, relationships, and certified metrics over warehouse models.
- Build and support governed natural-language data experiences using Snowflake Cortex Analyst, Cortex Search, or equivalent LLM-native query layers.
- Ground AI-enabled data experiences in certified data sources and semantic models, with appropriate access controls, evaluation, monitoring, and failure handling.
- Configure secure Model Context Protocol connections or comparable interfaces between governed data sources and internal AI tooling.
- Document data models, pipelines, business logic, operational procedures, and technical decisions comprehensively.
Must-Have Skills
- 5+ years of relevant experience.
- Hands-on Snowflake experience, including data modeling, datamarts, and data warehouse design.
- Strong applied understanding of columnar data architecture and Parquet, including their use in analytical workloads and the related performance and design tradeoffs.
- Hands-on dbt experience for data transformations.
- Strong Python experience, including object-oriented programming and data scripting.
- Hands-on Airflow experience for pipeline orchestration.
- Experience integrating REST APIs and ingesting data from external sources.
- Hands-on Google BigQuery querying and optimization experience.
- Experience securely handling sensitive data at large scale.
- Experience with real-time or near-real-time data processing from APIs, Google Sheets, or comparable sources.
- Strong SQL skills, including highly optimized queries.
- Comprehensive technical documentation skills.
- Advanced English communication skills, including the ability to explain personally delivered technical work and architecture decisions clearly.
Nice-to-Have Skills
- Experience designing semantic layers or semantic models that provide business-object abstraction over dbt and warehouse models.
- Hands-on experience personally building, deploying, or supporting Snowflake Cortex Analyst, Cortex Search, or an equivalent LLM-native AI-agent capability.
- Experience grounding AI agents in governed or certified data and applying access controls, evaluation methods, monitoring, and production support practices.
- Experience with Model Context Protocol or a similar tool-calling and context-exposure framework.
- Familiarity with prompt and context engineering for grounding AI agents in certified data sources.
Required Tools & Platforms
- Snowflake.
- dbt.
- Python and PySpark.
- Apache Airflow.
- Google BigQuery.
- SQL.
- Parquet.
- REST APIs.
- Terraform.
- AWS Glue, Amazon EMR, and Amazon S3.
Location, Time & Engagement
- Candidates must be based in an eligible LATAM location.
- Full US Central Time coverage is required.
- This is a contract engagement at 40 hours per week.
- The anticipated engagement runs through March 31, 2027.
- This is not currently a contract-to-hire opportunity.