About Terawatt Infrastructure

The once in a century transition to autonomous and electric vehicles is underway and will require a multi-trillion-dollar investment in energy and charging infrastructure, and the real estate to site it on. Terawatt is the leader in delivering large scale, turnkey charging solutions for companies rapidly deploying AV and EV fleets. Whether it’s an urban mobility hub, or a carefully located multi-fleet hub for semi-trucks, Terawatt brings the talent, capabilities, and capital to create reliable, cost-effective solutions for customers on the leading edge of the transition to the next generation of transport.

With a growing portfolio of sites across the US in urban hubs and along key logistics and transportation corridors and logistics hubs, Terawatt is building the permanent transportation and logistics infrastructure of tomorrow through a robust combination of capital, real estate, development, and site operations solutions. The company develops, finances, owns, and operates charging solutions that take the cost and complexity out of electrifying fleets.

At Terawatt, we execute humbly and with urgency to provide tailored solutions for fleets that delight our clients and support the transition of transportation.

Role Description

We are seeking a highly skilled and motivated Data Engineer to join our growing team. In this role, you will primarily focus on designing and implementing scalable and efficient data architectures to support our business needs. You will collaborate closely with data scientists, analysts, and other cross-functional teams to build and optimize data pipelines, ensuring that data is accessible, secure, and well-structured for analytics and reporting.

A key part of this role involves developing and maintaining data models, databases, and data lakes, while implementing robust data governance and quality assurance practices. You will drive the creation of efficient and scalable data infrastructures, ensuring alignment with the company's architecture standards and best practices.

Additionally, we are looking for someone who exhibits curiosity and a genuine willingness to build and maintain production data lake pipelines that turn raw time-series data into ML-ready features, training datasets, and batch predictions. Ensure data quality, reproducibility, and reliable retraining so ML outputs, like forecasts and risk scores, can be trusted by downstream systems.