Data Engineer

The Data Engineer is responsible for building and scaling the data and computational backbone that supports Arva’s ecosystem modeling and measurement, reporting, and verification platforms. This role sits within a multidisciplinary Data Science team and focuses on designing reliable, auditable, and scalable data systems that enable biogeochemical modeling and optimization at production scale.

In this role, the Data Engineer will design and maintain production-grade data pipelines that integrate diverse datasets including field measurements, management practices, soils, and weather with process-based ecosystem models. The role plays a critical part in ensuring data quality, reproducibility, and traceability so that scientific outputs can be translated into trusted, credit-grade results with real-world impact.

• Design, implement, and maintain scalable data pipelines supporting ecosystem and biogeochemical modeling
• Build reproducible workflows that generate standardized model inputs and manage outputs across space, time, and scenario analysis
• Integrate heterogeneous datasets, including field data, management data, soil data, and weather data, into modeling pipelines

• Develop and maintain cloud-based infrastructure to support modeling pipelines and optimization workflows
• Implement data storage solutions using relational, spatial, and object-based databases
• Support efficient data access and processing using platforms such as PostgreSQL, PostGIS, and cloud object storage

• Ensure data quality, versioning, traceability, and auditability to support measurement, reporting, and verification requirements
• Implement validation and monitoring processes to ensure reliability of model inputs and outputs
• Support transparent, repeatable workflows suitable for regulatory and credit market review

• Write clean, modular, and well-documented production code that supports maintainable and scalable data systems
• Apply software engineering best practices including testing, version control, and documentation
• Collaborate closely with Data Science and Technology teams to align data infrastructure with modeling, analytics, and production needs

• 3+ years demonstrated experience building and maintaining data pipelines for large, complex, and heterogeneous datasets
• Strong proficiency in Python and modern data engineering tools, with experience writing production-grade, testable code
• Experience working with cloud platforms, with AWS strongly preferred
• Familiarity with containerization tools such as Docker and version control systems such as GitHub
• Experience with relational and spatial databases, including PostgreSQL and PostGIS
• Experience working with geospatial data formats and spatial data processing
• Experience supporting scientific or ecosystem modeling workflows preferred
• Familiarity with workflow orchestration tools such as Airflow or Prefect preferred
• Bachelor’s or Master’s degree or equivalent experience in Data Engineering, Computer Science, Environmental Informatics, or a related field