Job Description
The role involves leading the scaling and reliability of Gridmatic's data infrastructure to support advanced machine learning models for energy price forecasting, handling large-scale data ingestion, improving data pipeline robustness, and enabling more granular and extensive data utilization to enhance energy market predictions.
Key Responsibilities
- Lead the scaling of data infrastructure to handle petabyte-scale weather and energy data for ML models
- Improve the reliability of data pipelines by addressing issues such as downtime, late-arriving data, and schema changes
- Ingest and manage external data sources to support advanced energy price forecasting models
Requirements
- Experience in scaling and improving the reliability of data infrastructure, including data pipelines and platforms.
- Ability to incorporate petabyte-scale weather data into machine learning models to increase data volume by a factor of 10-100x.
- Experience with increasing spatial granularity of price forecasting models.
- Proficiency in addressing real-world data ingestion problems such as downtime, late-arriving data, and changing schemas.
- Strong understanding of data pipeline reliability and strategies to improve it.
- Experience working with external data sources and managing data quality issues.
- Technical expertise in data engineering, including building and maintaining scalable data systems.
- Ability to work in a hybrid work environment, with in-person collaboration at the Cupertino office 3 days a week.
Benefits & Perks
Hybrid work policy requiring 3 days per week in the Cupertino office
Opportunity to work on complex real-world problems related to energy and climate
Involvement in large-scale data challenges and ML applications
Collaborative team environment with expertise in ML, energy, and optimization
Potential for professional growth through technical interviews and take-home projects
Ready to Apply?
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