AWS Data Engineer

Location: Argentina; Mexico City, Mexico City, Mexico

Department: Information Technology

Role Summary

ON.energy is building the power infrastructure that makes the AI era possible. Our systems are deployed across 2.5 GW of hyper-scale campuses, validated by top U.S. national labs, and certified for grid-safe operation by major utilities. You’ll be the fourth engineer on a growing data team, helping scale our AWS-based data lakehouse and working primarily with industrial data sources and high-volume time-series data. No prior experience in the energy sector is required, we’ll support you in learning the domain.

Key Responsibilities

• Build and maintain scalable ETL/ELT pipelines for batch and real-time processing, including high-frequency time-series ingestion.
• Evolve the Data Lakehouse — optimizing storage, performance, cost, and data consistency.
• Manage orchestration workflows with complex dependencies and error handling.
• Deliver production-ready, analytics-optimized datasets, engaging directly with end users to understand how they consume data.
• Implement data governance, security controls, and audit policies across the AWS ecosystem.
• Build monitoring, alerting, and data quality testing for platform reliability.

Key Requirements

• Bachelor’s degree in Computer Science, Computer Engineering, or a closely related discipline.
• 3+ years of hands-on Data Engineering on AWS, with real exposure to modern data lakehouse architectures.
• English at B2 or above. Daily work with English-speaking teams and written documentation.
• Production experience with open table formats, preferably Apache Iceberg (table design, partitioning, schema evolution). Coming from Delta Lake or Hudi? We’ll support you in transitioning.
• Core AWS data services: Glue, Athena, Lambda, and S3.
• Designing and maintaining ETL/ELT pipelines for batch and streaming workloads.
• Python (PySpark / Python Shell) and advanced SQL (window functions, CTEs, execution plan tuning).
• Data modeling for analytical workloads in a lakehouse context — medallion architectures, incremental loads, deduplication, historical backfills.
• Infrastructure as Code: Terraform or CloudFormation.

Preferred Experience

Step Functions · Kinesis · Lake Formation · DynamoDB · custom ETL with boto3, pyiceberg, pyarrow · CI/CD for Glue or Lambda · time-series and IoT/telemetry data at scale

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