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Salary: USD 180,000 - 230,000 / annual
Treeswift builds physical AI for field workers in the energy sector, empowering utilities to modernize field operations and increase productivity 10x. The company combines cutting-edge hardware (LiDAR, cameras, sensors), AI, and software to revolutionize work in challenging environments. Since launching in June 2024, Treeswift has grown rapidly and now works with three of the five largest US utilities, helping them reduce wildfire risk, manage vegetation-related outages, and accelerate storm recovery.
As Data Platform Engineer, you will design, build, and maintain data pipelines at scale using Apache Airflow 3 on Astronomer. You'll develop and evolve DAGs that orchestrate complex, multi-step workflows processing terabytes of real-world physical data across diverse file types—imagery, audio, point clouds, and more. Your work will span dozens of tasks with fan-out/fan-in patterns, Python and Kubernetes operators across generalized and specialized node pools, and dynamic DAG generation. You'll collaborate closely with the in-house ML team (whose feature pipelines and model deployment live in these DAGs) and coordinate with the hardware team on data ingestion and formats.
You will be the second dedicated data engineer on a small, highly collaborative team. Responsibilities include improving DAG design and execution, resource and cost tuning, reliability and observability, and contributing to how Treeswift runs Airflow and Kubernetes in the cloud. The role balances pipeline development with platform ownership; scope can be adjusted based on your interests and strengths.
In this early-stage environment, you'll wear multiple hats, working alongside ML engineers, hardware engineers, and software engineers. You'll help turn technically complex requirements from a critical industry into rich datasets that customers use for better-informed decisions. You'll partner closely with some of the largest utilities in the country and contribute to developing new workflows in work planning, construction, and disaster response.
The role is full-time, hybrid, based in Lower Manhattan with 2 days per week in-office (currently Tuesdays and Wednesdays).
REQUIREMENTS:
- Bachelor's degree in Computer Science, Computer Engineering, Math, or related field (or equivalent experience)
- 4+ years of data engineering or backend engineering experience with focus on pipelines, orchestration, or platform
- Hands-on experience building and maintaining production data pipelines (Airflow, Prefect, Luigi, or similar)
- Experience with cloud object storage and data-at-scale (AWS and S3 used; cloud experience required, prior AWS experience not required)
- Comfort with Kubernetes and container-based deployments: running workloads on K8s, resource and volume configuration, debugging pod/worker issues
- Ability to own work end-to-end: design, implement, test, and operate pipelines and related tooling
- Strong collaboration and communication skills; ability to work well with ML, hardware, and product stakeholders and explain tradeoffs clearly
- Python experience helpful but not required; willingness to learn on the job
NICE-TO-HAVES:
- Experience in early-stage or fast-moving environments with evolving scope and ownership
- Apache Airflow (especially 3.x) and/or Astronomer experience
- Geospatial data, imagery, LiDAR, or point cloud experience
- Interest in utilities, forestry, or field operations
