Data Engineer ID99039

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.

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

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.

- 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.

- 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.

- Upper-intermediate English level.

- 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.

- 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.