• Develop, enhance, and troubleshoot Mainframe batch processes using JCL, Easytrieve, and SAS. • Build and maintain automation and data processing scripts using Python. • Support distributed data processing workloads using Apache Spark and the PySpark API. • Write efficient SQL queries for data extraction, analysis, and transformation. • Work with Google Cloud Platform (GCP) services - primarily Cloud Storage - for data movement and storage management. • Collaborate with data analysts, engineers, and business teams to support data initiatives and enhance data workflows. • Participate in documentation, code reviews, and best practices for data and code quality. • Investigate data issues, perform root-cause analysis, and implement corrective actions.

• Conduct remote sensing analytics and modeling • Develop scalable analytical and reporting tools • Manage GIS data collection, storage, and version control across team members • Engage in strategic planning & process improvement • Provide support to collaborate with Land and Forestry teams • Manage and analyze large datasets off-line and in cloud computing and storage platforms • Analyze large and complex geospatial datasets and remote sensing data • Design and implement novel predictive, statistical, and machine learning models related to forestry, land use, carbon sequestration, biodiversity, conservation planning, and climate resilience • Automate statistical and geospatial analysis processes using Python, R, or other programming languages • Create clear and impactful reporting tools to communicate geospatial information and insights • Maintain and update internal geospatial databases, ensuring data quality, consistency, and version control • Integrate data from Land, Forestry, and Carbon teams to support commercial initiatives • Ensure high standards of data accuracy and ethical use in all geospatial analyses and models • Conduct quality control checks on geospatial datasets • Provide technical mapping support to other team members as needed • Work closely with Land, Forestry, and Carbon teams to uncover new operational insights • Identify opportunities to improve geospatial workflows and contribute to the development of best practices • Support research and development efforts in geospatial analytics and remote sensing applications.

• Design and maintain standardized data schemas used across different data sources and storage systems • Define data contracts and models to ensure consistent representation of entities such as users, groups, resources, and permissions • Develop and maintain schema evolution and versioning processes to support iterative product development • Ensure data models are optimized for both transactional and analytical workloads • Collaborate with engineering and product teams to align data models with business logic and reporting requirements • Design and optimize ClickHouse schemas for analytical and time-series workloads • Maintain PostgreSQL schemas for metadata, configuration, and application-level data • Develop indexing, partitioning, and retention strategies that balance performance, scalability, and cost • Define transformation specifications to ensure consistency between raw and analytical data layers • Establish naming conventions, data types, and relationship standards for all stored data • Implement validation and normalization checks to ensure incoming data adheres to defined schemas • Partner with QA and product teams to verify that stored data accurately represents system behavior and business intent • Maintain clear documentation and metadata definitions for all datasets and structures • Manage schema migrations and versioning through CI/CD workflows • Collaborate with DevOps teams to deploy and monitor databases in Kubernetes-based environments • Use Infrastructure as Code tools (Helm, Terraform, or similar) for consistent database provisioning • Support observability and monitoring for data performance and reliability.

• Write and ship a lot of code. • Work directly with analysts and stakeholders to refine requirements, nail down logic, and debug and qualify produced data sets to ensure they meet the underlying business needs. • Drive architectural decisions and thoughtfully balance trade-offs in system design. • Lead cross-functional data initiatives. • Champion high standards in data quality, security, and discoverability. • Translate complex technical challenges into clear solutions. • Design and develop high-performance data pipelines. Adhering to SDLC, including CI/CD and best practices. • Identify and drive opportunities to optimize and/or scale existing parts of our stack. • Utilize tooling and automation to improve developer efficiency. • Monitor data systems to ensure quality and availability while seeking to drive down costs. • Contribute to the team processes and community. • Mentor other Data Engineers. • Be part of our innovation and transformation story.