AgileEngine is an Inc. **** 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 USIf 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 ROLEWe 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 DOBuild 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 HAVES3+ 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 HAVESExperience 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 BENEFITSProfessional 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: Versátil schedule with remote and office options.Data Engineer (Marketing‐oriented) | Remote for Marketing Agency for EducationOur client is a fast‐growing performance marketing agency specializing in mission‐driven organizations across higher education and nonprofit sectors.
Since launching in ****, the agency has built a strong reputation for combining strategic thinking, creative execution, and data‐driven marketing to help universities and organizations achieve measurable growth.Company OverviewOur client is a fast‐growing performance marketing agency specializing in mission‐driven organizations across higher education and nonprofit sectors.
Since launching in ****, the agency has built a strong reputation for combining strategic thinking, creative execution, and data‐driven marketing to help universities and organizations achieve measurable growth.Your RoleThe Senior Data Engineer sits at the center of the agency's Data and Analytics function, connecting every paid media platform and CRM the agency touches into a single, reliable source of truth.
You'll own the full path data takes: from a client's ad accounts and CRM, through Adverity, into a BigQuery warehouse, through modular dbt/Dataform transformations, and out to the Looker Studio dashboards and automated Google Sheets reports the team and its partners rely on daily.
Reporting to the Data and Analytics Manager, you're joining at a pivotal moment — the agency is scaling its roster of education and nonprofit partners, and this role is instrumental in evolving the automated reporting suite that makes that growth possible without sacrificing data quality or client trust.You'llOwn data ingestion & pipeline architecture — connect and maintain API pipelines from paid media platforms (Meta, Google Ads, LinkedIn, TikTok, programmatic) into Adverity, build custom Python connectors for anything not natively supported, and architect scalable pipelines from source system through to dashboardOwn CRM integration — build and maintain reliable CRM data connections (Slate, Salesforce, HubSpot) so lead and enrollment data flows cleanly into the warehouseOwn the data warehouse — route, optimize, and store raw and processed data in Google BigQuery; design efficient schemas for complex, multi‐source marketing data (granularity, normalization, slowly changing dimensions); manage partitioning, clustering,



and storage strategy to control query cost and performanceOwn transformation & mapping — build SQL schemas that map, blend, and organize multi‐channel data accurately for each client, in modular, version‐controlled, documented dbt or Dataform pipelines, with standardized naming conventions and metric definitions across platforms and clientsOwn CI/CD & data segregation — maintain CI/CD pipelines (GitHub Actions, automated testing) for robust, version‐controlled releases, and enforce strict data segregation across multi‐client environmentsEnable the reporting layer — create clean, performant data views built specifically for fast Looker Studio dashboards and automated Google Sheets reports, and partner with the Data and Analytics Manager and client teams to translate reporting requirements into warehouse modelsDrive data quality — monitor pipeline health, resolve API breaks, minimize latency, implement validation frameworks and automated testing, troubleshoot discrepancies to root cause across source/warehouse/BI layers, and proactively flag data quality risks to leadershipYou Bring5+ years of hands‐on data engineering experience, ideally in a marketing or agency environmentStrong experience configuring datastreams, transformations, and destinations in Adverity or a comparable ETL platform (Fivetran, Supermetrics, Funnel)Intermediate to advanced SQL, with proven BigQuery experience in transformation, partitioning, and modelingExpertise in dbt or Dataform, managing modular, version‐controlled, documented SQL transformation pipelinesPython proficiency for custom API connections and pipeline automationExperience with CRM data flows and integrations (Slate, Salesforce, and/or HubSpot)A deep understanding of marketing metrics (ROAS, CPA, CAC) and paid platform schemas, with a proven track record of structuring data to optimize Looker Studio performanceProficiency with Git and collaborative development workflows, and proven ability to manage multi‐client data environments with strict data segregationAn analytical, solution‐driven mindset with a growth orientation, and strong communication skills that turn complex data workflows into clear narratives for non‐technical audiences — education marketing experience (traditional or online) is especially valuedBonus PointsExperience integrating AI‐driven insights or machine learning models into marketing data pipelines to sharpen predictive analytics and data utilityFamiliarity using AI or LLM tools to optimize performance forecasting, automated audience segmentation, or marketing workflowsExperience connecting, managing, and maintaining APIs for generative AI platforms within a marketing data architectureExperience using AI/ML‐driven anomaly detection to monitor pipelines and proactively flag data quality issuesWhat's OfferedRemote role with the flexibility that comes with a distributed, async‐friendly teamCompetitive compensation based on experience and locationA pivotal, high‐visibility seat on a scaling Data & Analytics team, with real ownership over the agency's reporting infrastructureThe chance to build the data backbone for an agency whose partners are education and nonprofit organizations working to build a better worldA culture built around finding a better way, playing as a team of A‐players, and treating results as the engine rather than the goalDirect impact: the pipelines and models you build directly shape how partners see (and trust) their own performance dataInterview ProcessApplication Review (resume + a few questions)Screening Call with Atomic HRHiring Team Interview (walkthrough of a pipeline or data model you've built)Technical Assessment (a real‐world data engineering case study)Final Interview with company leadership6BigQueryWelcome kit.Healthy Break.Welcome kit.Healthy Break.
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📌 Data Engineer (Buenos Aires)
🏢 Ryz Labs
📍 Buenos Aires