Remote Data Engineer: DBT, Snowflake & BI Pipelines

Devconnectplatform; Ciudad de Mendoza; Remote; ARS 169,994,000 - 231,811,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation

Exciting projects

Flextime

Job summary

AgileEngine in the United States 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 design production-grade data models, implement ingestion and orchestration with Airbyte or Prefect, and ensure data quality, security, and performance across the warehouse. Strong SQL and Python, plus an upper-intermediate English level, are required.

Qualifications

• 3+ years in analytics engineering, data engineering, or a closely related role, including hands-on work on production data pipelines that other systems or customers depended on.
• Solid dbt experience: modeling, tests, macros, and incremental models.
• Strong SQL and working Python.
• Data modeling fundamentals: dimensional modeling, data marts, grain and conformance.
• Experience with a modern cloud warehouse (Snowflake preferred).
• Experience with ingestion tooling and orchestration (Airbyte, Prefect, or equivalents).
• Familiarity with agentic coding tools and an understanding of why guardrails around their output matter.
• Deep curiosity about the business: you want to understand what the data means and how customers use it, not just how it flows.
• Software engineering discipline for analytics code: Git, code review, CI/CD.
• Can take a multi-sprint piece of work from spec to production with normal oversight, and can explain tradeoffs to technical and non-technical stakeholders.

Responsibilities

• Build and maintain the dbt pipelines, data marts, and warehouse models that serve in-app reporting, spend analytics, AI features, and internal analytics, treating them as production systems.
• Translate complicated procurement business logic into maintainable, well-tested pipeline code, and notice when a model is valid-looking but wrong.
• Build models within our data mart architecture, following the chapter's conventions for grain, conformed dimensions, and incremental patterns, and propose improvements when you find them.
• Contribute to and help buildout the semantic layer that our internal MCP servers and AI agents query, so metrics and dimensions are defined once and answered consistently.
• Maintain the boundary between PII and agent/reporting-safe data.
• Build and maintain the FastAPI services that expose reporting marts to customers.
• Build and maintain ingestion pipelines (Airbyte, OneSchema, S3) into the warehouse, and support our Forward Deployed Engineering function in bringing customer data into our systems.
• Implement data quality checks: freshness, schema and reconciliation tests, and alerting, so problems surface before customers find them.
• Implement metric definitions faithfully, and flag conflicting or ambiguous definitions to the chapter rather than guessing.
• Keep documentation and lineage current so that humans and AI agents can both find and trust what's in the warehouse.
• Tune Snowflake performance and cost within the models you build.
• Use agentic coding tools in your daily work, within the guardrails (tests, conventions, review patterns) the team maintains, and understand why those guardrails exist.
• Share what you learn through code review and team discussion.
• Communicate clearly with engineering, product, and customer-facing teams about what the data means and what it can and can't do.

Skills

dbt

SQL

Python

Snowflake

Airbyte

Prefect

Data modeling

ETL pipelines

Tools

Airbyte

Prefect

Job description

AgileEngine in the United States 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 design production-grade data models, implement ingestion and orchestration with Airbyte or Prefect, and ensure data quality, security, and performance across the warehouse. Strong SQL and Python, plus an upper-intermediate English level, are required.