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Neara is building engineering-grade digital twins of electricity grids using advanced machine learning and physics-enabled simulation. The company helps utilities across four continents understand infrastructure challenges, optimize investments, and build resilient energy systems by simulating extreme weather and structural stress at network-wide scale.
As a Data Engineer on the Deployment Team, you will transform raw utility and geospatial data into high-accuracy digital twin models of electricity networks. You will work with GIS, LiDAR, and asset datasets, configure models using Neara's proprietary schema language, and ensure engineering-grade accuracy throughout the deployment process.
Key responsibilities:
- Contribute to end-to-end deployment of the Neara platform for utility customers worldwide
- Ingest and manipulate massive datasets (LiDAR, GIS, asset databases) using Neara's schema language and configuration framework
- Perform rigorous QA/QC to ensure digital twins accurately reflect real-world physics and customer specifications
- Build custom reports and dashboards within the platform to drive actionable insights for utility engineers
- Work closely with Product and Engineering teams to provide feedback on process and tool improvements
Your work directly impacts the safety and reliability of global power grids, helping utilities prevent wildfires and prepare for climate-driven weather events. You'll gain expert-level proficiency in engineering-grade 3D modeling and exposure to cutting-edge digital twin technology.
Requirements:
- Proven experience as a Data Engineer, Implementation Engineer, Integration Engineer, Software Engineer, or similar role
- Proficiency in at least one major programming language (SQL, Python, or similar) with demonstrated ability to learn new languages
- Excellent written communication skills with experience contributing to internal knowledge bases and external user manuals; ability to articulate technical configurations clearly to diverse audiences in fluent English
- Background in engineering, mathematics, software engineering, computer science, or related field preferred
- Familiarity with GIS, CAD geoprocessing, or LiDAR data beneficial
- Ability to break down complex data discrepancies and systematically reconfigure models to ensure accuracy
