#136209 - Data Engineer - HR Analytics & AI-Ready Data

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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.

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、仓库模型和分析数据集，以支持人员洞察。该角色将动手实践的数据工程与数据建模、平台可靠性、语义层设计以及面向商业智能工具和AI赋能接口的受治理访问相结合。

最优秀的候选人将展现出应用深度，而不仅仅是对工具的熟悉。他们应能够解释其亲自构建或支持的数据平台、管道、存储模式和AI赋能能力背后的架构选择与权衡。

强烈优先考虑具有企业经验的候选人。

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构建提取、转换和加载基础设施。
- 集成来自API、云系统、Google Sheets和其他结构化来源的数据。
- 创建和维护Snowflake仓库模型、数据集市和分析就绪数据集。
- 在设计分析工作负载的存储、文件布局和查询性能策略时，应用扎实的数据工程基础原则。
- 开发自动化数据质量测试并改进内部数据工程流程。
- 监控生产管道并帮助维持99.5%的正常运行时间目标。
- 在仓库模型之上设计语义视图、本体层、业务友好实体、关系和认证指标。
- 使用Snowflake Cortex Analyst、Cortex Search或等效的LLM原生查询层构建和支持受治理的自然语言数据体验。
- 将AI赋能的数据体验建立在经过认证的数据源和语义模型之上，并配备适当的访问控制、评估、监控和故障处理。
- 配置受治理数据源与内部AI工具之间的安全Model Context Protocol连接或类似接口。
- 全面记录数据模型、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。
- 动手实践的Snowflake经验，包括数据建模、数据集市和数据仓库设计。
- 对列式数据架构和Parquet有扎实的应用理解，包括它们在分析工作负载中的使用以及相关的性能和设计权衡。
- 动手实践的dbt数据转换经验。
- 较强的Python经验，包括面向对象编程和数据脚本编写。
- 动手实践的Airflow管道编排经验。
- 具有集成REST API和从外部来源摄取数据的经验。
- 动手实践的Google BigQuery查询和优化经验。
- 具有大规模安全处理敏感数据的经验。
- 具有从API、Google Sheets或类似来源进行实时或近实时数据处理的经验。
- 较强的SQL技能，包括高度优化的查询。
- 全面的技术文档编写技能。
- 高级英语沟通技能，包括能够清晰解释亲自交付的技术工作和架构决策的能力。

加分技能

- 具有设计语义层或语义模型的经验，能够在dbt和仓库模型之上提供业务对象抽象。
- 具有亲自构建、部署或支持Snowflake Cortex Analyst、Cortex Search或等效LLM原生AI代理能力的动手实践经验。
- 具有将AI代理建立在受治理或认证数据之上，并应用访问控制、评估方法、监控和生产支持实践的经验。
- 具有Model Context Protocol或类似工具调用和上下文暴露框架的经验。
- 熟悉提示和上下文工程，以将AI代理建立在经过认证的数据源之上。

所需工具与平台

- Snowflake。
- dbt。
- Python和PySpark。
- Apache Airflow。
- Google BigQuery。
- SQL。
- Parquet。
- REST API。
- Terraform。
- AWS Glue、Amazon EMR和Amazon S3。

地点、时间与聘用方式

- 候选人必须位于符合条件的LATAM地点。
- 需要全程覆盖美国中部时间。
- 这是一个每周40小时的合同聘用。
- 预计聘用期持续至2027年3月31日。
- 目前这不是一个合同转正的机会。

⚑ 岗位信息收录自公开招聘页（ATS 官方），以雇主官网为准