Mid-Level Data Engineer: dbt, Snowflake & AI Pipelines

AgileEngine, LLC.; Ciudad de Mendoza; Híbrido; ARS 185.966.000 - 278.949.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Professional growth

Competitive compensation

Exciting projects

Flextime

Descripción de la vacante

AgileEngine is seeking a Mid-level Data Engineer to build and maintain dbt pipelines and Snowflake data marts behind customer-facing reporting, spend analytics, and AI features. You will translate complex procurement logic into reliable pipelines and contribute to a scalable data architecture.

Responsibilities include building production-grade pipelines, maintaining data quality, optimizing Snowflake performance, and shaping the semantic layer for analytics and AI agents.

Formación

• 3+ years in analytics engineering, data engineering, or a closely related role with production data pipelines.

Responsabilidades

• Build and maintain the dbt pipelines, data marts, and warehouse models serving in-app reporting, spend analytics, and AI features.
• Translate procurement business logic into maintainable, tested pipeline code.
• Build models within data mart architecture and propose improvements.
• Contribute to the semantic layer for consistent metrics and dimensions.
• Maintain boundary between PII and reporting-safe data.
• Build and maintain FastAPI services exposing reporting marts to customers.
• Ingest data via Airbyte, OneSchema, and S3 into the warehouse.

Conocimientos

dbt pipelines

SQL

Python

Data modeling

Snowflake

Airbyte/Prefect

Production pipelines

English (upper-intermediate)

Herramientas

Snowflake

Descripción del empleo

AgileEngine is seeking a Mid-level Data Engineer to build and maintain dbt pipelines and Snowflake data marts behind customer-facing reporting, spend analytics, and AI features. You will translate complex procurement logic into reliable pipelines and contribute to a scalable data architecture.

Responsibilities include building production-grade pipelines, maintaining data quality, optimizing Snowflake performance, and shaping the semantic layer for analytics and AI agents.