Data Engineer - Uruguay - Zona Franca

Mutt Data SRL

Job Overview

Location

Uruguay

Job Type

Full-time

Full Job Description

📋 Description

• Design, build, and maintain scalable data pipelines that ingest, transform, and deliver structured and unstructured data from multiple sources to support machine learning models and analytics platforms.
• Collaborate with data scientists and machine learning engineers to operationalize models by creating robust, production-grade data workflows and feature stores.
• Develop and optimize ETL/ELT processes using modern tools such as Apache Airflow, Spark, Kafka, or similar frameworks to ensure high throughput and low latency.
• Implement data quality monitoring, validation, and alerting systems to detect anomalies, missing data, or schema drift in real-time data streams.
• Work with cloud-based data infrastructure (e.g., AWS, GCP, or Azure) to provision, configure, and manage data storage solutions including data lakes, warehouses, and streaming platforms.
• Automate data pipeline deployments and CI/CD workflows to enable frequent, reliable updates with minimal manual intervention.
• Document data schemas, pipeline architectures, and operational procedures to ensure knowledge sharing and maintainability across cross-functional teams.
• Participate in code reviews, technical design discussions, and sprint planning to align data engineering efforts with product and ML roadmap priorities.
• Troubleshoot and resolve data pipeline failures, performance bottlenecks, and integration issues across distributed systems.
• Stay current with emerging data engineering tools, best practices, and industry standards to continuously improve system reliability and efficiency.
• Contribute to the design of data governance policies, including access controls, lineage tracking, and compliance with data privacy regulations.
• Work closely with product and engineering teams to translate business requirements into technical data solutions that drive product decisions.
• Support the migration of legacy data systems to modern, scalable architectures while ensuring zero data loss and minimal downtime.
• Maintain a strong focus on data integrity, reproducibility, and auditability across all data products and machine learning pipelines.
• Engage in a fully remote, asynchronous work environment with team members across time zones, requiring strong self-management and communication skills.
• Contribute to open-source initiatives and internal tooling projects that enhance the team’s data engineering capabilities.

🎯 Requirements

• Proven experience building and maintaining production data pipelines using ETL/ELT tools such as Apache Airflow, Spark, or Kafka
• Strong proficiency in at least one major cloud platform (AWS, GCP, or Azure) for data storage and processing
• Expertise in SQL and experience with data warehousing solutions (e.g., BigQuery, Redshift, Snowflake)
• Experience working in a startup or fast-paced environment with evolving requirements and minimal documentation
• Ability to write clean, testable, and maintainable code in Python or Scala
• Familiarity with containerization (Docker) and orchestration tools (Kubernetes)

🏖️ Benefits

• Fully remote work with flexible hours across time zones
• Competitive salary in USD
• Equity participation in the startup
• Budget for professional development and learning resources
• Access to co-working spaces in Uruguay’s Zona Franca
• Annual team retreats in Uruguay

Skills & Technologies

Remote

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Mutt Data SRL

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About Mutt Data SRL

Argentina-headquartered data consultancy that designs, builds and runs cloud-native data platforms and AI products for mid-market and enterprise clients across North and South America. Core services span data engineering, MLOps, analytics engineering and custom machine-learning model development, delivered by multidisciplinary teams using modern open-source stacks on AWS, GCP and Azure. Founded in 2014, the company maintains offices in Buenos Aires, Córdoba, Montevideo and Raleigh, and is a certified partner of Databricks, Snowflake, Google Cloud and AWS.

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