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

Job Description
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.

Qualifications

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.

Additional Information
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.