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 Arkestro's 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 trade-offs to technical and non-technical stakeholders.
• Upper-intermediate English level. 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
• Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
• Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
• Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
• Meaningful, modern projects: build impactful products using modern technologies alongside integral teams and leading brands
• Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
• Well-being & support: access local well-being programs and people-focused support tailored to your location

📌 Data Engineer (Colombia)
🏢 Agileengine
📍 Colombia