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Treefera is building Environmental Intelligence systems that turn satellite and ground-truth signals into actionable evidence for organizations protecting forests and managing supply-chain risk.
You will own the design and evolution of data models, pipelines, and APIs that power this platform. This is a full-stack data systems role: you'll ship services end-to-end, from customer problem to production API, and live with the consequences of your architectural decisions. You work from problems rather than tickets, picking your approach and explaining the trade-offs. You extend existing systems thoughtfully—refactoring where it pays, resisting rewrites where they don't—and carry work through maintenance and operational reliability.
Key responsibilities:
- Design data models and APIs that other engineers build on
- Build the systems behind Environmental Intelligence: pipelines, APIs, and data infrastructure
- Ship services end-to-end from customer question to production, including data model, pipeline, and API
- Own technical decisions in your scope; extend the platform within its constraints
- Mentor less experienced engineers and improve team velocity through code review and tooling
- Demo your work and enable adoption across sales, support, and customer teams
- Partner day-to-day with Product, Customer Solutions, and the Science/AI team
Expected impact milestones:
- 30 days: ship something small to production; understand platform edges and legacy corners
- 60 days: own a piece of Environmental Intelligence end-to-end, including open-ended problems
- 90 days: release to customers; see your work in use
- 6 months: your work shapes team standards, documentation, and tooling
- 1 year: environmental evidence reaches decision-makers faster through your contributions
You use AI in your workflow or are ready to, and can articulate what you kept, threw away, and why.
REQUIREMENTS:
- Demonstrated experience taking a production Python service or pipeline through significant architectural change (re-model, re-platform, or rewrite)
- Proven ability to design data models and APIs that others build on
- Problem-driven approach: you pick the approach, make trade-off calls, and explain reasoning
- Experience extending existing systems rather than defaulting to rewrites
Desirable:
- Geospatial or Earth Observation data experience (GIS, remote sensing, Postgres/PostGIS)
- Scientific Python (NumPy, GeoPandas, Xarray) or pipeline orchestration (Dagster)
- Familiarity with TypeScript, React, or NestJS (occasional, not daily use)
