Funga

Data Scientist

• Perform robust statistical analyses of experimental and observational forest data
• Evaluate and enhance current experimental design by performing power analyses and applying statistical models to multi-year trials
• Rapidly prototype and deploy models connecting forest productivity to soil microbiomes and environmental factors, accounting for spatial and temporal dependencies
• Enhance our carbon accounting system to incorporate incoming field-collected, genomic, and remotely sensed data
• Build and validate pipelines for assessing the effectiveness of satellite and drone datasets for capturing on-the-ground measurements
• Work closely with internal stakeholders on Growth, Applied Science, and other teams to ensure that insights meet their needs and accelerate their work
• Travel up to 10-15% of the time for team offsite events, data collection, and stakeholder meetings as needed

Data Engineer

• Engineer Data Delivery & Modeling
• Build transformation layers using SQL and Python.
• Lead implementation of internal data apps.
• Architect geospatial workflows.
• Engineer automated validation pipelines for data quality.
• Build and maintain containerized connectors.
• Collaborate on core database architecture with Data Infrastructure Engineer.

Infrastructure Engineer

• Own the Stack: Architect and maintain our central storage (PostgreSQL/PostGIS, SQLite) and cloud environment (AWS/GCP), leveraging ECS, Lambda, and S3.
• Modern DevOps: Standardize environments using Docker, CI/CD pipelines, and Infrastructure as Code to automate the testing and deployment of data services.
• Scale for Performance: Optimize data models and database performance management for extensibility as our genomic and geospatial inputs scale.
• Build Lean Ingest: Design and automate scalable ELT/ETL pipelines for genomic, geospatial, and tabular data from sources like Survey123, ArcGIS, and Asana.
• QA/QC at Scale: Build automated validation pipelines to ensure data integrity and version control from the moment it hits our system.
• Internal Enablement: Support scientists and operational teams by designing the data models that power internal modeling workflows, dashboards, and reporting.
• System Connectivity: Develop lightweight APIs and connectors to sync data between our core infrastructure and downstream applications (e.g. Asana, ArcGIS, and internal dashboards).