The Role
Build and optimize ETL pipelines into Snowflake using Airflow and AWS. Develop modular dbt models with tests and documentation, improve orchestration (Airflow/Astronomer), collaborate with analytics and stakeholders on data modeling, and ensure high-quality, scalable data delivery for analytics and downstream applications.
Summary Generated by Built In
Remote position, only for professionals based in Latam.
We're looking for a data engineer for one of our clients' team. You will help enhance and scale the data transformation and modeling layer. This role will focus on building robust, maintainable pipelines using dbt, Snowflake, and Airflow to support analytics and downstream applications. You’ll work closely with the data, analytics, and software engineering teams to create scalable data models, improve pipeline orchestration, and ensure trusted, high-quality data delivery.
Key Responsibilities:
- 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
Core Skills & Experience:
- 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
Nice to Have:
- Experience with Oracle
-Familiarity with AWS services such as DMS, Kinesis, and Firehose for ingesting and transforming data
- Segment: Familiarity with event data and related flows, piping data in and out of Segment
Skills Required
- Located in Latin America (Latam)
- Hands-on experience developing dbt models at scale, including macros, snapshots, testing frameworks, and documentation; familiarity with dbt Cloud or CLI
- Strong SQL skills and understanding of Snowflake architecture, including query performance tuning, cost optimization, and semi-structured data
- Experience designing, implementing, and optimizing data pipelines using Airflow (managing DAGs, scheduling, retry logic, failure handling)
- Experience with AWS services for data ingestion and processing
- Proficient in dimensional modeling and building reusable data marts for analytics and operational use cases
- Experience working with analytics and business stakeholders to define source-to-target transformations
- Familiarity with Astronomer (Airflow platform)
- Experience with Oracle
- Familiarity with AWS DMS, Kinesis, and Firehose
- Familiarity with Segment and event data flows
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The Company
What We Do
Where startups soar! We're not just building startups - we're crafting the future. From ideation to execution, we provide the blueprint for startup success. We specialize in nurturing startups from the ground up, and supercharging existing ones with our top-tier technical talent solutions. Join us at Ryz Labs, where we turn promising ideas into thriving businesses. Let's shape the future of innovation together!
