Data Engineer LATAM
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• Develop a scalable data platform integrating multiple sources for easy access.
• Design and enhance data tools (orchestration, governance, data lakehouse, BI, etc.).
• Ensure smooth operation of data systems for analysts, scientists, and engineers.
• Optimize data pipelines (ingestion, processing, and output) in a microservices environment.
• Build, maintain, and monitor ETL/ELT processes .
• Troubleshoot and improve the performance, scalability, and reliability of the data infrastructure (S3, Apache Iceberg, ClickHouse).
• Collaborate cross-functionally with data scientists, analysts, and backend engineers to understand data needs and deliver solutions.
• Implement and champion data quality, governance, and security best practices across the platform. Requirements
• 3+ years of experience as a Data Engineer or in a similar data infrastructure role.
• Strong proficiency in SQL and hands-on experience with data modeling.
• Experience with data lake/lakehouse architectures (e.g., Apache Iceberg, S3, or similar).
• Experience with analytical / columnar databases (e.g., ClickHouse or similar).
• Experience building and orchestrating ETL/ELT pipelines .
• Strong programming skills in Python and/or Scala/Java.
• Experience working within a microservices architecture and cloud environments (AWS preferred).
• Self-motivated, strong multitasking skills, and a demonstrated team player.
• Excellent communication skills and the ability to work both independently and collaboratively.
• Hands-on experience with Apache Spark (or similar technologies) for large-scale data processing.
• Professional proficiency in written and spoken English.
• Note: this role is focused on batch data processing (not real-time streaming). Nice to Have
• Experience working with and contributing to open-source data platforms and tools.
• Familiarity with BI and visualization tools (e.g., Superset, Looker, Tableau, Metabase, or similar).
• Experience with containerization and orchestration (Docker, Kubernetes).
• Experience with infrastructure-as-code and CI/CD practices.
• Experience with AWS EMR and running Apache Spark workloads in a cloud environment.
• Experience leveraging AI-assisted development tools (e.g., GitHub Copilot, Cursor, or similar) to boost engineering productivity.
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