Data Analyst

• Infrastructure Management: Design, build, and maintain scalable ELT/ETL (Extract, Transform, Load/Extract Transform, Load) pipelines to ingest and transform high-frequency grid data, ensuring data integrity and lineage.
• Data Pipeline Automation: Develop advanced Python scripts for data orchestration, automating the extraction of simulation results from production cost and capacity expansion models (e.g., PlanOS, PLEXOS).
• Data Modelling & Warehousing: Manage relational database schemas (PostgreSQL) and implement data-lake architectures to optimize storage and query performance for complex power system time-series data.
• Visualization & Business Intelligence (BI): Design and develop high-performance interactive dashboards using Power BI or Tableau that translates complex technical data into clear, actionable insights for key decision-makers. Should be able to use PowerBI, Co-pilot and AI agents to create reports.
• Collaborate with power systems engineers to bridge the gap between simulation outputs and front-end visualization, ensuring seamless integration of grid reliability and economic metrics.
• Innovation: Actively contribute to GE Vernova’s internal data-driven business initiatives, promoting a culture of high-quality data engineering and technical innovation.

• Bachelor’s degree in Computer Science, Data Engineering, Electrical Engineering, or related quantitative field.

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, with proficiency on Python-based automation, database management (PostgreSQL or similar), and sophisticated data visualization.

• Demonstrated proficiency in building end-to-end ELT workflows and managing large-scale datasets, preferably within the energy or utility sector.
• Expertise in Power BI, including advanced data modelling (schemas), data analysis optimization, and Direct Query/Import strategy management.
• Strong analytical skills with an ability to manage multiple complex data priorities across different regions simultaneously.
• Passion for solving challenging technical problems in a collaborative, cross-functional environment.

• Knowledge of power market modelling, simulation tools (e.g., PlanOS or similar), and an understanding of electric power system fundamentals is a plus.
• Proficiency in Cloud data environments (e.g., Databricks, Azure Data Factory, Snowflake) and containerization (Docker/Kubernetes).
• Active participation in industry professional data or engineering societies.
• Master’s or PhD degree in Computer Science, Data Engineering, Electrical Engineering, or related quantitative field.