The posting, in ON.energy's own words
archived Sep 11, 2026ON.energy is building the backbone of energy and AI infrastructure powering grid-safe data centers and mission-critical facilities. The company supplies and operates hyperscale power systems that solve the toughest resilience challenges, delivering custom solutions for AI data centers, mission-critical facilities, and front-of-the-meter assets. ON recently announced a 5GW partnership, with 3GW currently under construction across multiple hyperscale data center campuses. With patented technology and proprietary software, ON.energy develops projects worldwide that set new benchmarks for resilience.
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.
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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: or CloudFormation. Preferred Experience Step Functions · Kinesis · Lake Formation · DynamoDB · custom ETL with boto3, pyiceberg, pyarrow · for Glue or Lambda · time-series and IoT/telemetry data at scale #LI-AD1