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Listing evidence and provenance
- Source
- Greenhouse View original listing
- Freshness
- Open in the current public read path; this is not an employer guarantee.Last observed October 5, 2026
- Classification
- Modern power-system classification not yet published for this listing.Legacy source industry field: Energy Management
- Pay provenance
- Pay not available
Job Description
ON.energy is building the power infrastructure that makes the AI era possible.
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.
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
- #LI-AD1
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