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Data Analyst

GE Vernova · Artificial Intelligence · Industrial Machinery · Relocation: Yes · Bachelor's Degree · Python · Tableau · Power BI · ETL/ELT · Azure Data Factory · 2 months ago

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Salary

≈ $9.5k – $21k per year (Estimated)

Location

In office (Bengaluru)

Seniority

Junior · 2+ years exp

Employment

Full-Time

Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Sep 18, 2026. GE Vernova scores B on the Alion truth index.

Overview

Company

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GE Vernova

GE Vernova is an American energy company created in April 2024 when General Electric split into three, taking the power, wind and electrification businesses. It builds and services gas turbines that generate a substantial share of the world's electricity, onshore and offshore wind turbines, grid equipment including transformers and high-voltage transmission, and nuclear technology through GE Hitachi and its BWRX-300 small modular reactor. Headquartered in Cambridge, Massachusetts, the company sits at the centre of two simultaneous demand shocks: the electrification of transport and industry, and the electricity requirements of large-scale artificial intelligence data centres.

gevernova.com · HQ: Cambridge, United States · Hydroelectric · Wind Energy · Industrial Machinery · Power Grid · Nuclear Energy · 5000+ employees · Est. 2024

Tech stack of GE Vernova

Job Description Summary

The 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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