Companies in this catalog do not charge applicants. Never send money to apply.
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.