Data Platform Engineer

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.

Since our first pilot with a utility in June 2024, we've grown fast, now working with three of the five largest utilities in the US. To date, 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.

You are a skilled and motivated Data Platform Engineer. You will:

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  Bachelor’s degree in Computer Science, Computer Engineering, Math, or a related field (or equivalent experience).

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  4+ years of data engineering or backend engineering experience with a focus on pipelines, orchestration, or platform.

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  Hands-on experience building and maintaining production data pipelines (e.g. Airflow, Prefect, Luigi, or similar). We use Python for our pipeline environment, machine learning, and developer tooling; we don’t require Python expertise and are happy for you to learn on the job.

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  Experience with cloud object storage and data-at-scale (we use AWS and S3; cloud experience is required, but prior AWS experience is not).

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  Comfort with Kubernetes and container-based deployments in practice: running workloads on K8s, resource and volume configuration, and debugging pod/worker issues.

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  Ability to own work end-to-end: design, implement, test, and operate pipelines and related tooling. You are comfortable picking up new parts of the stack when needed.

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  Strong collaboration and communication; you work well with ML, hardware, and product stakeholders and can explain tradeoffs clearly.

• Experience in early-stage or fast-moving environments where scope and ownership evolve.

• Experience with Apache Airflow (especially 3.x) and/or Astronomer.

• Experience with geospatial data, imagery, lidar, or point clouds.

• Interest in utilities, forestry, or field operations and how data pipelines support those domains.