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Sr Staff Analytics Engineer

GE Vernova LogoGE Vernova
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Salary

For candidates applying to a U.S. based position, the pay range for this position is between $123,200.00 and $205,200.00.

Location

Greenville, SC; Schenectady, NY

Posted

Today

Wind energy generates some of the most complex, high-volume data in the energy sector. If you want to build the analytics infrastructure that helps keep turbines turning and fleets performing at their best, this role puts you at the center of that work.

GE Vernova is looking for a Senior Staff Analytics Engineer to join the Wind Engineering Fleet Performance Management (FPM) Fleet Data & Analytics team. You will design and deliver the data pipelines, analytics products, and monitoring capabilities that power engineering decisions across GE Vernova's global wind fleet. Your work will directly support turbine reliability, fleet performance, and the engineering teams who depend on trusted, scalable data every day.

What you'll do

  • Own the development and operation of analytics solutions across the full lifecycle — from design through deployment, monitoring, and continuous improvement.
  • Build and maintain scalable data pipelines and transformation workflows that turn raw turbine and fleet data into reliable, production-ready analytics assets.
  • Develop analytics products, dashboards, and Key Performance Indicators (KPIs) that surface insights on turbine reliability, fleet performance, and engineering outcomes — using tools such as Power BI, Tableau, Plotly Dash, or Streamlit.
  • Design and evolve reusable data models and analytics components for cross-functional wind use cases, driving standardization and quality across the team.
  • Monitor production solutions for data quality, pipeline health, and performance, leading root cause analysis and troubleshooting on complex datasets.
  • Apply software engineering and DevOps best practices — including Continuous Integration/Continuous Delivery (CI/CD), automated testing, version control, and release management — to deliver solutions that are maintainable and built to last.
  • Partner with wind engineering, product, digital, and operations teams to translate complex domain needs into practical, scalable technical solutions.
  • Guide and mentor teammates, contribute to backlog and release planning within Agile delivery structures, and help raise the standard of engineering and analytics practice across the team.

What you'll bring

You are a technically strong analytics engineer who is equally comfortable working in the data layer and collaborating with domain experts. You bring production discipline to your craft and the communication skills to operate effectively across engineering, product, and business stakeholders.

Required

  • A formal education and subsequent Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Analytics, Mathematics, Statistics, or a related STEM field is nice to have, but we are most interested in your total experience and professional achievements.
  • 8+ years of professional experience in analytics engineering, data engineering, software engineering, or a closely related technical field.
  • Strong hands-on experience with Python and SQL for building production-quality analytics and data solutions.
  • Experience designing, building, and supporting data pipelines, data models, and analytics workflows in industrial, engineering, or enterprise environments.
  • Experience developing dashboards and data applications using Power BI, Tableau, Plotly Dash, Streamlit, or similar tools.
  • Experience applying CI/CD, version control, testing, and release management practices in data and analytics environments.
  • Experience working in Agile/Scrum delivery teams, including backlog management, sprint planning, and cross-functional coordination.

Preferred

  • Experience working with wind turbine, SCADA, sensor, operational, or event data in an analytics or engineering context.
  • Hands-on experience with modern data platforms such as Databricks, Spark, Snowflake, Redshift, BigQuery, or equivalent distributed processing tools.
  • Familiarity with cloud data platforms and platform-oriented engineering approaches for scalable analytics delivery.

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