Data Engineer – Data Pipelines, Modeling

🕒 May 8

🇦🇷 Argentina – Remote

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 50%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Ryz Labs

WebsiteLinkedInAll Job Openings

11 - 50 employees

💼 Consulting

📦 Logistics

📣 Marketing

Consulting • Logistics • Marketing

Ryz Labs is a company focused on building startups from the ground up and assisting other startups in scaling their operations. They provide top-tier technical talent solutions to ensure that emerging businesses can thrive in a competitive market.

📋 Description

• Design, implement, and optimize data pipelines that extract, transform, and load data into Snowflake from multiple sources using Airflow and AWS services • Build modular, well-documented dbt models with strong test coverage to serve business reporting, lifecycle marketing, and experimentation use cases • Partner with analytics and business stakeholders to define source-to-target transformations and implement them in dbt • Maintain and improve our orchestration layer (Airflow/Astronomer) to ensure reliability, visibility, and efficient dependency management • Collaborate on data model design best practices, including dimensional modeling, naming conventions, and versioning strategies

🎯 Requirements

• dbt: Hands-on experience developing dbt models at scale, including use of macros, snapshots, testing frameworks, and documentation. Familiarity with dbt Cloud or CLI workflows • Snowflake: Strong SQL skills and understanding of Snowflake architecture, including query performance tuning, cost optimization, and use of semi-structured data • Airflow: Solid experience managing Airflow DAGs, scheduling jobs, and implementing retry logic and failure handling; familiarity with Astronomer is a plus • Data Modeling: Proficient in dimensional modeling and building reusable data marts that support analytics and operational use cases