Data Engineer: dbt, Snowflake & AI Pipelines

AgileEngine, LLC.; Santiago de Querétaro; Hybrid; MXN 1,125,000 - 1,688,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation

Exciting projects

Flextime

Job summary

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. The role requires solid dbt experience, ingestion/orchestration tools, and 3+ years in production data pipelines.

You will translate complex procurement business logic into maintainable pipeline code, contribute to the semantic layer, manage data boundaries, and ensure data quality and performance in Snowflake.

Qualifications

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

Responsibilities

• Build and maintain dbt pipelines, data marts, and warehouse models for in-app reporting and analytics.
• Translate complex procurement business logic into maintainable pipeline code.
• Build models within data mart architecture with proper grain and conformance.
• Contribute to the semantic layer used by internal systems and AI agents.
• Maintain boundary between PII and safe data for reporting.
• Develop and maintain FastAPI services exposing reporting marts to customers.
• Ingest data via Airbyte, OneSchema, and S3 into the warehouse and support data onboarding.
• Implement data quality checks for freshness, schema tests, and alerting.

Skills

SQL

Python

Dimensional modeling

Data engineering

Git / CI/CD

Tools

dbt

Airbyte

Prefect

Snowflake

FastAPI

Job description

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. The role requires solid dbt experience, ingestion/orchestration tools, and 3+ years in production data pipelines.

You will translate complex procurement business logic into maintainable pipeline code, contribute to the semantic layer, manage data boundaries, and ensure data quality and performance in Snowflake.