Senior Bi & Data Solutions Engineer

Bluelight; Ciudad de Mendoza; Hybrid; ARS 185,819,000 - 278,728,000; Full time

Benefits offered by this job

Professional growth

Competitive USD-based pay

Exciting projects

Flextime

Job summary

AgileEngine in Mendoza ( hybrid/remote ) is seeking a Mid-level Data Engineer to build and maintain dbt pipelines and Snowflake data marts behind customer reporting, spend analytics, and AI features.

You will work with ingestion tools like Airbyte or Prefect, write SQL and Python, and model data with dimensional modeling. Upper-intermediate English is required; focus on production pipelines, observability, and collaboration with product and customer teams.

Qualifications

• 3+ years in analytics engineering or related role with production data pipelines.
• Solid dbt experience: modeling, tests, macros, incremental models.
• Strong SQL and Python skills.
• Data modeling fundamentals: dimensional modeling, data marts, grain and conformance.
• Experience with a modern cloud warehouse (Snowflake preferred).
• Experience with ingestion/orchestration tools (Airbyte, Prefect or equivalents).
• Upper-intermediate English level.
• Git, CI/CD and software engineering discipline for analytics code.
• Ability to take multi-sprint work from spec to production and explain tradeoffs.

Responsibilities

• Build and maintain dbt pipelines, data marts, and warehouse models for reporting, spend analytics, AI features, and internal analytics.
• Translate complex procurement business logic into maintainable, well-tested pipeline code.
• Build models within the data mart architecture and propose improvements.
• Contribute to 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.
• Ingest data into the warehouse (Airbyte, OneSchema, S3) and support customer data onboarding.
• Implement data quality checks, freshness, schema tests, and alerting.
• Document lineage and ensure metric definitions are consistent.
• Tune Snowflake performance and cost; share learnings via code reviews.

Skills

Analytics engineering

SQL

Python

dbt

Snowflake

Airbyte

Prefect

Data modeling

Git/CI-CD

English

Tools

Airbyte

Prefect

FastAPI

AWS S3

Job description

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 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

• 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.

Requirements

5+ years of professional experience in Data Engineering or Data Platform Engineering Strong hands-on experience operating and developing solutions on Snowflake, including data-layer design, warehouse performance, access patterns, and production troubleshooting. Advanced SQL skills and strong experience with dbt for transformation, testing, documentation, lineage, and controlled deployment. Experience operating managed ingestion tools such as Fivetran or HVR and supporting custom data-ingestion pipelines. Hands-on experience with AWS data services, particularly S3 and event-driven or file-based ingestion patterns. Experience orchestrating and troubleshooting data workloads with Argo Workflows on Kubernetes, or comparable workflow-orchestration technologies. Proficiency in Python or a comparable language for data engineering, automation, and operational tooling. Strong understanding of data modeling, pipeline dependencies, schema evolution, backfills, data validation, and production data quality. Experience with observability, logging, alerting, and incident-management practices for production data platforms. Demonstrated ability to lead complex incident resolution, perform root-cause analysis, and convert findings into preventive improvements. Ability to make well-reasoned technical decisions, identify tradeoffs, estimate work, and guide improvements across a complex platform. Experience mentoring engineers and collaborating effectively with Cloud / DevOps, Analytics, Security, Governance, and business stakeholders. Strong written and verbal English communication skills, with the ability to work directly with client stakeholders. Availability to work from 9:00 AM to 6:00 PM Pacific Time and participate in an agreed on-call rotation.

• Welcome kit.
• Healthy Break.