Data Engineer

Ryz Labs

Rosario

Remote

ARS 139,086,000 - 200,903,000

Full time

3 days ago

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Benefits offered by this job

Professional growth

Competitive USD-based pay

Exciting projects

Flextime

Job summary

AgileEngine is seeking 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 role requires strong SQL, Python, and dbt, plus experience with Airbyte or Prefect and modern cloud warehouses.

You will own ingestion, modeling, and semantic layer work in a remote setting. You will communicate with engineering, product, and client teams, ensuring data quality, cost control, and performant

Qualifications

Minimum 3+ years in analytics or data engineering with production pipelines.
Solid dbt experience: modelling, tests, macros, incremental models.
Strong SQL and Python skills.
Fundamentals in dimensional modeling and data marts.
Experience with a modern cloud warehouse (Snowflake preferred).
Experience with ingestion tooling and orchestration (Airbyte, Prefect).
Familiarity with guardrails for data quality and CI/CD practices.

Responsibilities

Build and maintain dbt pipelines, data marts, and warehouse models for reporting and analytics.
Translate business logic into maintainable, tested pipeline code.
Develop models within data mart architecture and propose improvements.
Contribute to semantic layer and consistent metric definitions.
Maintain boundary between PII and reporting data.
Build and maintain FastAPI services exposing reporting marts.
Ingest data via Airbyte/OneSchema/S3 into the warehouse and support customer data onboarding.
Implement data quality checks, tests, and alerting.
Document lineage and ensure reliability for humans and AI agents.

Skills

dbt experience

SQL

Python

dimensional modeling

cloud data warehouse (Snowflake)

Airbyte or Prefect

Git & CI/CD

communication

Tools

dbt

Airbyte

Prefect

FastAPI

GitHub Actions

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.

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.

Data Engineer (Marketing‑oriented) | Remote for Marketing Agency for Education

Our client is a fast‑growing performance marketing agency specializing in mission‑driven organizations across higher education and nonprofit sectors. Since launching in 2020, 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 Overview

Your Role

The 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'll

Own 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 dashboard
Own CRM integration — build and maintain reliable CRM data connections (Slate, Salesforce, HubSpot) so lead and enrollment data flows cleanly into the warehouse
Own 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 performance
Own 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 clients
Own 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 environments
Enable 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 models
Drive 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 leadership

You Bring

5+ years of hands‑on data engineering experience, ideally in a marketing or agency environment
Strong 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 modeling
Expertise in dbt or Dataform, managing modular, version‑controlled, documented SQL transformation pipelines
Python proficiency for custom API connections and pipeline automation
Experience 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 performance
Proficiency with Git and collaborative development workflows, and proven ability to manage multi‑client data environments with strict data segregation
An 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 valued

Bonus Points

Experience integrating AI‑driven insights or machine learning models into marketing data pipelines to sharpen predictive analytics and data utility
Familiarity using AI or LLM tools to optimize performance forecasting, automated audience segmentation, or marketing workflows
Experience connecting, managing, and maintaining APIs for generative AI platforms within a marketing data architecture
Experience using AI/ML‑driven anomaly detection to monitor pipelines and proactively flag data quality issues

What’s Offered

Remote role with the flexibility that comes with a distributed, async‑friendly team
Competitive compensation based on experience and location
A pivotal, high‑visibility seat on a scaling Data & Analytics team, with real ownership over the agency's reporting infrastructure
The chance to build the data backbone for an agency whose partners are education and nonprofit organizations working to build a better world
A culture built around finding a better way, playing as a team of A‑players, and treating results as the engine rather than the goal
Direct impact: the pipelines and models you build directly shape how partners see (and trust) their own performance data

Interview Process