agile engine 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 fast api services that expose reporting marts to customers.
- build and maintain ingestion pipelines (airbyte, one schema, 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 (fast api 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, tech talks, 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 : adaptable schedule with remote and office options.

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📌 Data engineer id99039 (Rosarito)
🏢 Agileengine
📍 Rosarito