Data Engineer ID99039

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 tradeoffs 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 and benefits 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 global 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 Job Type: Full-time Work Location: Remote

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.

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.

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 global 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 Job Type: Full-time Work Location: Remote