We are seeking a Senior 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 LATAM.

- 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.