Data Engineer

GridCARE solves data center developers' most urgent bottleneck — immediate access to power — through a pioneering physics-based generative AI platform that unlocks gigawatts of near-term capacity from today’s grid. Partnering with utilities, GridCARE applies a proprietary playbook to create additional network capacity on existing transmission infrastructure without costly upgrades or multi-year delays. Founded at Stanford’s Doerr School and backed by leading investors in Energy and AI, GridCARE is working with major technology and data center companies to accelerate interconnection requests for large-load and data center projects.

As a fast-growing startup, we are seeking a skilled Data Engineer to help develop data-driven solutions that have real-world impact.

We are looking for a Data Engineer with hands-on experience in data ingestion into cloud environments, finding and retrieving new datasets, and performing analysis on various data types, including time-series and non-relational data. In this role, you’ll streamline data processes, ensure data quality and accessibility, and collaborate with UI/UX and AI leads to leverage data for innovative solutions.

This is a unique opportunity to combine deep data engineering and scientific expertise with cutting-edge AI solutions applied to solve the most impactful speed to power in the energy industry.

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  Design and implement data ingestion pipelines into cloud environments, ensuring efficient and scalable data pipelines.

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• Bachelor’s or higher in Computer Science, Data Science, Electrical Engineering, or a related quantitative field.

• Proficiency with cloud data platforms (e.g., AWS, GCP, Azure) and data warehousing solutions.

• Strong coding skills in Python, SQL, including experience with data manipulation libraries (e.g., Pandas, NumPy) and database interaction and version control (git).

• Ability to write well-documented, tested, and maintainable code.

• Conduct code reviews and contribute to continuous integration and deployment workflows.

• Strong understanding of data modeling, ETL processes, and data governance.

• Knowledge of time-series analysis techniques and non-relational database concepts.

• Excellent problem-solving, communication, and collaboration skills in a fast-paced startup environment.

• 2+ years of experience in data engineering, data science, or related areas.

• Experience integrating and tuning 3rd party AI agents.

• Able to leverage AI code development tools such as Cursor, Copilot, and Claude Code to accelerate development.

• Experience working with energy data, including Virtual Power Plants (VPPs), batteries, and control systems.

• Experience with Kubernetes, Terraform, Helm charts and deploying data applications in the cloud.

• Familiar with DevOps pipelines, tools, and requirements.