• Design, build, and maintain scalable data pipelines using Databricks and Apache Airflow • Support data analysts by ensuring they have clean, structured, and well-documented data • Manage and optimize Tableau dashboards, ensuring seamless data exports and reporting • Develop ETL workflows and data integrations using Python • Monitor data quality, optimize performance and cost efficiency • Take responsibility for the smooth operation and reliability of the data platform • Work with cloud-based data solutions (AWS)

Role Description American Forests is seeking a part-time, paid undergraduate or graduate student intern for June–July 2026 to support data management, metadata development, and GIS-related tasks associated with the launch of the Forest Innovation Platform. - Organizing datasets from the Climate Risk Viewer. - Integrating datasets into the Forest Innovation Platform Data Hub. - Developing clear, standardized metadata for platform data layers. - General data processing and GIS support as needed. Qualifications - Current enrollment at Colorado State University. - Experience with GIS (e.g., ArcGIS, QGIS). - Strong data management and organizational skills. - Clear and effective technical writing ability. Requirements - This position reports to the Director of Climate Science. - No direct reports. Benefits - Compensation: $20.00 per hour. - This position is part-time and non-exempt. - Not eligible for benefits. Working Conditions - This is a remote position with a preference for candidates based in Fort Collins, Colorado. - Up to 10% travel throughout the U.S. and/or to American Forests’ headquarters in Washington D.C. may be required. - Ability to remain in a stationary position (sitting or standing) for extended periods. - Ability to operate a computer and other office equipment. - Reasonable accommodation may be provided to support individuals across the full range of abilities and experiences.

• Lead, hire, and develop a team of data engineers, including performance management and career growth • Own end-to-end delivery, including sprint planning, prioritization, and execution • Establish engineering standards and ensure high-quality, consistent output • Partner with cross-functional stakeholders to align data initiatives with business priorities • Design and evolve scalable data architecture across ingestion, transformation, storage, and consumption layers • Build and maintain high-volume ETL/ELT pipelines for real-time and batch data • Lead data modeling, governance, and data quality practices • Drive adoption of AI and automation across data engineering workflows • Partner with data science teams to operationalize models and embed AI into data products • Evaluate and implement emerging tools with a focus on practical impact

• This role focuses on building the data foundations that enable advanced analytics and machine learning at scale. • You will design and develop data pipelines and architectures that support real-time insights and AI-driven applications across the business. • Working with large, complex datasets, you will ensure data is reliable, accessible, and optimized for both analytics and machine learning use cases. • Develop data models and warehouse schemas optimized for analytics and machine learning. • Build and maintain data ingestion frameworks using streaming systems (Kafka, Kinesis) and batch processing (Spark, Airflow). • Build and manage feature stores to support ML model training and inference. • Integrate data from multiple sources into a unified architecture. • Implement data quality validation frameworks and monitoring systems. • Optimize data pipelines for performance, scalability, and cost efficiency.