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 Senior Data Engineer to join our growing team. In this role, you will design and implement scalable and effective 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 development of scalable data infrastructure aligned with company architecture standards and best practices.

This role also requires curiosity and a commitment to building and maintaining production data lake pipelines that transform raw time‑series data into ML‑ready features, training datasets, and batch predictions. This includes ensuring data quality, reproducibility, and reliable retraining so ML outputs—such as forecasts and risk scores—can be trusted by downstream systems.

Problems You Will Solve

- Turning messy operational data into reliable signals by building pipelines that transform noisy, incomplete, and high‑volume time‑series data into trusted datasets for analytics, product features, and ML workflows
- Design a resilient lakehouse platform by architecting a scalable Databricks‑based platform that support both streaming and batch workloads while ensuring governance, observability, and reliability
- Enable production‑ready ML pipelines by creating reproducible workflows, reliable feature datasets,



and batch prediction pipelines that downstream systems can depend on
- Enable self‑service analytics and ML by building infrastructure and abstractions that allow analysts, engineers, and data scientists to independently explore and use data
- Scale a platform for product and analytics by designing systems that support operational product features, internal reporting, and ML use cases without compromising performance or data quality

Core Responsibilities
- Architect and evolve a Databricks‑based data platform that serves as the scalable foundation for product features, internal reporting, and ML workflows.
- Set technical standards for modeling raw data into clean, reliable datasets, ensuring high integrity and point‑in‑time accuracy for both BI and ML applications.
- Build and maintain self‑service tooling and infrastructure abstractions that improve the developer experience for data producers, analysts, and data scientists.
- Design and optimize high‑performance ETL/ELT pipelines using Delta Live Tables and Structured Streaming to handle seamless ingestion from diverse data sources.
- Own platform observability, testing, and proactive monitoring to ensure the performance and reliability of critical data delivery and pipeline health.
- Architect and enforce data security, compliance, and access controls by implementing Unity Catalog and IAM (Identity and Access Management) best practices across the enterprise.
- Build and maintain production‑grade pipelines that transform raw data into ML‑ready features, training datasets, and reliable batch predictions.
- Lead Infrastructure as Code (

📌 Senior Data Engineer (Toronto)
🏢 Terawatt Infrastructure
📍 Toronto