Data Platform Engineer

About Treeswift: In the face of rising threats, increasing pressure on affordability, and unprecedented demand for power, Treeswift empowers energy companies to modernize their field work to meet the growth and challenges ahead. We build physical AI for the field worker: whether on foot or in a vehicle, our technology is an ironman suit for engineers, linemen, and vegetation crews: same worker, same boots on the ground, now operating at 10x productivity. Our platform is powered by cutting edge hardware, sensors (LiDAR, camera, etc…), AI and software designed to revolutionize work in the toughest environments. Our technology has enabled our customers to reduce wildfire risk, regulatory and outage risk from vegetation, avoid delays and cost overruns in new construction, and accelerate recovery from severe storms. To tackle this challenge, we are bringing together a team of mission-driven experts with deep industry experience in robotics (Penn, Caltech, CMU) and enterprise software development (Palantir, Stripe, Oracle, MongoDB). We have raised funding from leading investors including Penny Pritzker’s Inspired Capital. We are headquartered in midtown Manhattan, with additional offices in San Francisco and Philadelphia. Our growth is only accelerating. We’re looking for deeply curious and highly ambitious people who want to have a real world impact. Come build the future of (field) work with us. About the role You are a skilled and motivated Data Platform Engineer. You will: - Design, build, and maintain data pipelines at scale. We run Apache Airflow 3 on Astronomer with pipelines that process terabytes of real-world physical data across many file types—imagery, audio, point clouds, and more. You will develop and evolve DAGs that orchestrate complex, multi-step workflows: dozens of tasks, fan out/in in pipelines, Python and Kubernetes operators split across generalized and specialized node pools, and dynamic DAG generation. You will work closely with our in-house ML team (feature pipelines and model deployment live in these DAGs) and coordinate with our hardware team on ingestion and formats. Scope is a mix of pipeline development and platform ownership and we are happy to adjust the scope and balance of responsibilities based on your interests and strengths. - Help us scale and harden our data platform. We have one dedicated data engineer today; you will be the second. The broader engineering team is highly collaborative and you will work with members of the full-stack and machine learning teams. We are looking for someone to improve DAG design and execution, resource and cost tuning, reliability and observability, and contribute to how we run Airflow and Kubernetes in the cloud. If you enjoy writing pipelines and improving the platform that runs them, this role has room for both. - Stay curious, collaborative, and cross-functional. We are a small team where many people wear multiple hats. You will work alongside ML engineers,…