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.
Originally posted on