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What you’ll bring

All qualifications

Core experience

Bachelor’s degree in Computer Science, Data Engineering, Electrical Engineering, or related quantitative field.
Proficiency in Cloud data environments (e.g., Databricks, Azure Data Factory, Snowflake) and containerization (Docker/Kubernetes).
2-5 years of relevant experience, with proficiency on Python-based automation, database management (PostgreSQL or similar), and sophisticated data visualization.
Master’s or PhD degree in Computer Science, Data Engineering, Electrical Engineering, or related quantitative field.
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.

Preferred experience

Knowledge of power market modelling, simulation tools (e.g., PlanOS or similar), and an understanding of electric power system fundamentals is a plus.

Qualification wording

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

Proficiency in Cloud data environments (e.g., Databricks, Azure Data Factory, Snowflake) and containerization (Docker/Kubernetes).

2-5 years of relevant experience, with proficiency on Python-based automation, database management (PostgreSQL or similar), and sophisticated data visualization.

Master’s or PhD degree in Computer Science, Data Engineering, Electrical Engineering, or related quantitative field.

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.

Knowledge of power market modelling, simulation tools (e.g., PlanOS or similar), and an understanding of electric power system fundamentals is a plus.

Education & alternatives

- 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. Additional Information

Tools in this posting

Python
Azure
Databricks
PostgreSQL
Power BI
Snowflake
Tableau
Docker
Kubernetes

Source

- 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.

- Bachelor’s 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.

- 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.

- 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.

- 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.

- 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.

Job description

View original posting ↗

Job Description SummaryThe Data Analyst will develop technical architecture and automation support to regional and global GE Vernova Consulting Services team. This role employs a data-centric solutions approach to transform raw power system datasets into high-fidelity actionable insights, enhancing our tactical and strategic market position through robust analytical pipelines.

Job Description

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 Vernova’s internal data-driven business initiatives, promoting a culture of high-quality data engineering and technical innovation.

Required Qualifications

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

Additional Information

Relocation Assistance Provided: Yes

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Source & posting history

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Pay

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Location & working pattern

Bengaluru

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Open source at gevernova.wd5.myworkdayjobs.com ↗Read the full description ↓

Status in our records: Active
First seen by us: Sep 18, 2026
Recorded sightings: 25
Last seen by us: Oct 9, 2026

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