Staff Data Engineer
Remote
Full Time
#Engineering
#Remote Sensing
#AI
#Python
#Compute
#Data Engineering
#Dask
#Flyte
#Kubernetes
#GCP
#Debugging
We are Pachama, a mission-driven company that uses satellite imaging and artificial intelligence to measure carbon captured in forests. Our marketplace connects responsible companies and individuals with high-quality carbon credits from projects that protect and restore forests worldwide. We are backed by investors including Breakthrough Energy Ventures, Amazon Climate Fund, Chris Sacca, Saltwater Ventures, and Paul Graham, and we were recently named the number-one most innovative AI company by Fast Company. We are hiring a Staff Data Engineer to lead the development of the data systems that power our AI and remote-sensing products.
Responsibilities
- Build and scale ingest pipelines, storage systems, and compute frameworks that turn multi-terabyte geospatial and remote-sensing datasets into actionable insights for forest-carbon projects.
- Provide technical leadership on cross-functional initiatives by shaping the vision for our data and compute platform and guiding teams through implementation.
- Mentor engineers on best practices for data pipelines and distributed compute while contributing hands-on code that improves efficiency and quality across the engineering organization.
Requirements
- Senior-level experience leading large, cross-team engineering efforts in data engineering, including ingest, storage, orchestration, and large-scale compute.
- Strong Python programming skills, solid software-engineering practices, and proficiency with debugging, profiling, version control, and system design.
- Familiarity with distributed-compute technologies and concepts such as CPU/GPU interactions, latency and throughput optimization, and multiprocessing; our stack includes Dask and Flyte on Kubernetes and GCP.
- Comfort working in a fast-paced startup environment with rapid iteration and a genuine passion for environmental sustainability.
What we offer
- Fully remote work with a preference for candidates within three hours of Pacific time to support cross-functional collaboration.
