Principal Geospatial Data Management Engineer

Requisition ID:  92195 

 

NextEra Energy Resources is one of America’s largest wholesale electricity generators, harnessing diverse energy sources to power progress. We deliver tailored energy solutions that fuel economic growth, strengthen communities, and help customers achieve their energy goals. Ready to make a lasting impact? Take the next step in your career with us!

 

Position Specific Description

About NextEra Energy: NextEra Energy is a Fortune 200 company that owns Florida Power & Light Company, the largest electric utility in the United States, serving approximately 12 million residents in Florida. Our diverse energy portfolio includes natural gas, nuclear, renewable energy, and battery storage solutions. With a strong commitment to meeting America's evolving energy needs sustainably, NextEra Energy Resources, LLC, is a leader in energy infrastructure development. Ready to make a meaningful impact? Join our team to elevate your career.

 

The Principal Geospatial Data Engineer for the Enterprise NEE.GIS Geospatial DataOps team will drive innovation by integrating cutting-edge geospatial data management technologies at scale into sustainable geospatial analytics. This role involves leading cross-functional teams to design, build, and deliver advanced geospatial data management and analytics solutions, enhancing operational efficiency and strategic decision-making.

 

We place a strong emphasis on expertise in current technologies and processes, coupled with a proven track record of innovation. Ideal candidates will have extensive experience in geospatial data management and analytics, and demonstrate a passion for developing groundbreaking analyses, processes, or tools utilizing advanced technology and techniques.

 

Key Responsibilities

 

The Principal Geospatial Data Engineer serves as a technical leader, architecting and implementing sophisticated geospatial data engineering solutions. This role demands deep expertise in spatial data technologies, large-scale distributed systems, and modern data engineering practices to deliver mission-critical geospatial intelligence at enterprise scale.

 

• Architect, design, and maintain robust, scalable data pipelines and infrastructures for geospatial and big data applications maintaining a focus on performance and the ultimate end-user product experience.
• Lead the development and optimization of ETL processes for ingesting, cleaning, transforming, and storing large volumes of geospatial and tabular data.
• Design, build, and interact with API-driven, service-to-service web services to enable integration across a suite of products and capabilities.
• Collaborate with backend and platform engineers to ensure secure, reliable, and scalable service-to-service communication.
• Translate complex analytics and business questions into actionable, production-grade data solutions.
• Collaborate closely with data scientists, analysts, and business stakeholders to deliver high-impact data products.
• Drive the adoption and optimization of cloud-based data solutions (e.g. AWS).
• Ensure data quality, integrity, and security across all stages of the data lifecycle.
• Mentor and provide technical guidance to junior data engineers and business team members.
• Communicate technical details and insights clearly to both technical and non-technical audiences, including leadership.
• Proactively recommend and implement improvements to existing data infrastructure and software programs.
• Stay current with industry trends and emerging technologies in geospatial data engineering.

Preferred Qualifications:

• Possess expert knowledge of coordinate reference systems, including their reprojections and conversions, with a focus on understanding the implications of accuracy and performance impact on data pipelines and published data products.
• Strong experience with geospatial analytics and working with geospatial data formats (e.g., SDE, LAS, LAZ, COPC, GeoTIFF, Shapefiles, KML, GeoVideo, Etc).
• Proven experience in national or global scale data challenges.
• Experience with data streaming, real-time data processing, or cloud-native geospatial solutions.
• Understanding of Python; familiarity with version control systems like Git and Agile methodologies.
• Expertise in Safe Software FME and Esri Enterprise GIS platforms, as well as cloud computing environments like AWS, leveraging Kubernetes.
• Knowledge of geospatial libraries and tools (e.g., Esri, GDAL, PDAL, PostGIS, GeoPandas, Shapely, etc).
• Awareness of machine learning, deep learning, and artificial intelligence applications related to geospatial data engineering workflows.