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.