Data Engineer ID99039

AgileEngine, LLC.; Región Centro; Presencial; MXN 480.000 - 640.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Professional growth

Competitive compensation (USD)

Exciting projects

Flextime

Descripción de la vacante

AgileEngine, LLC. in Guadalajara, Mexico is seeking a Mid-level Data Engineer to build and maintain dbt pipelines and Snowflake data marts powering customer reporting, spend analytics, and AI features.

You will own production pipelines, ensure data quality, and collaborate with product and engineering teams across the data platform.

The role requires 3+ years in analytics/data engineering, strong SQL and Python, dbt experience, and a pragmatic approach to scalable data models.

Formación

• 3+ years in analytics engineering, data engineering, or a closely related role with production data pipelines.
• 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 guardrails around their output.
• Deep curiosity about the business and how data is used by customers.
• Software engineering discipline for analytics code: Git, code review, CI/CD.
• Ability to take multi-sprint work from spec to production with oversight and explain tradeoffs.

Responsabilidades

• Build and maintain the dbt pipelines, data marts, and warehouse models powering in-app reporting, spend analytics, AI features, and internal analytics.
• Translate procurement business logic into maintainable, well-tested pipeline code and identify wrong models.
• Build models within the data mart architecture, following grain and conformance conventions.
• Contribute to and help build the semantic layer queried by MCP servers and AI agents.
• Maintain the boundary between PII and agent/reporting-safe data.
• Build and maintain FastAPI services exposing reporting marts to customers.
• Build and maintain ingestion pipelines (Airbyte, OneSchema, S3) into the warehouse and support Forward Deployed Engineering.
• Implement data quality checks: freshness, schema tests, reconciliation, and alerting.
• Define metric definitions faithfully and flag ambiguities for review.
• Keep documentation and lineage current for humans and AI.
• Tune Snowflake performance and cost within the models.
• Use agentic coding tools with guardrails and share learnings through code reviews.
• Communicate data meaning clearly with engineering, product, and customer-facing teams.

Conocimientos

SQL

Python

Data modeling

Analytical thinking

Cloud data warehousing

Herramientas

dbt

Snowflake

Airbyte

Prefect

FastAPI

Descripción del empleo

Guadalajara, Mexico | Posted on 11/03/2026

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for 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 mandatory requirements are solid dbt experience (modeling, tests, macros, and incremental models), ingestion and orchestration tools such as Airbyte or Prefect, 3+ years in data or analytics engineering on production pipelines, strong SQL and Python, dimensional modeling, Snowflake or a similar cloud warehouse, and an upper-intermediate English level.

WHAT YOU WILL DO

• 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 that 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.

MUST HAVES

• 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.

NICE TO HAVES

• Experience building or maintaining API services on top of warehouse data (FastAPI or similar).
• Experience with a semantic layer or governed metric definitions that multiple consumers rely on, including for LLM-driven or agent-driven querying.
• Experience exposing warehouse data to customers through embedded analytics or customer-facing APIs.
• Experience with data observability tooling.
• Experience supporting customer data onboarding or implementation teams.
• Experience deploying data pipelines across multiple regions and managing PII or sensitive data.
• Knowledge of procurement and/or supply chain data models.
• Prior experience in enterprise software and startups.

PERKS AND BENEFITS

• Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
• Exciting projects: Modern solutions with Fortune 500 and top product companies.
• Flextime: Flexible schedule with remote and office options.