Summary

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.

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.
• 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 governed natural-language data experiences using Snowflake Cortex Analyst, Cortex Search, or equivalent LLM-native query layers.
• 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.
• 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.

Nice-to-Have Skills

• Experience designing semantic layers or semantic models that provide business-object abstraction over dbt and warehouse models.
• Experience with Snowflake Cortex Analyst, Cortex Search, or an equivalent LLM-native query layer.
• 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.
• 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.

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