Data Analyst

Roles and Responsibilities

• 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 Vernovas internal data-driven business initiatives promoting a culture of high-quality data engineering and technical innovation.

Required Qualifications

• Bachelors degree in Computer Science Data Engineering Electrical Engineering or related quantitative field.
• 2-5 years of relevant experience 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.

Desired Characteristics

• 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.
• Masters or PhD degree in Computer Science Data Engineering Electrical Engineering or related quantitative field.