• Perform enterprise-level statistical analysis to investigate trends, patterns, and relationships using statistical methods/tests to reach actionable conclusions. • Develop, train machine learning models to apply algorithms that support predictive analytics, classification tasks (e.g., land cover change), and operational forecasting • Develops enterprise-level information technology solutions by analyzing, coding, testing, and implementing new software and by modifying/enhancing existing software applications. • Coordinates with business analysts and/or users to determine requirements. Performs moderately complex analyses of system requirements and of existing business processes and information systems. • Collaborates with architects to determine the design of quality technical solutions. • Reviews systems under development and related documentation. • Resolves customers’ issues and responds to suggestions for improvements and enhancements. Participates in the development of software. • Performs code reviews and assists in more complex systems planning, scheduling and implementation.

• Develop and implement data processing workflows and pipelines to support solutions development and business intelligence needs. • Design and develop scalable and effective data solutions to support the flow and integration of data across systems. • Use big data technologies like Azure Data Lake, Synapse Analytics, and Data Factory to handle and analyze large volumes of data. • Implement data validation and cleansing procedures to ensure the highest level of data quality and integrity.

• Build robust automation processes for seamless data integration • Create and manage complex data workflows using Apache Airflow • Utilize MS Power Automate to streamline data processes • Develop customized SQL queries for efficient database solutions • Integrate multiple data sources into a customized data mart • Lead data science projects from problem definition to implementation • Meet with sales leaders to understand business challenges • Lead and manage complex projects ensuring timely delivery • Collaborate with cross-functional teams to achieve analytics goals

• Own Gold-layer design and delivery, including facts, dimensions, domain data marts, and governed KPI implementations on Databricks. • Build and maintain the enterprise semantic layer through curated views, governed semantic models, metric definitions, and reusable patterns for trusted business consumption. • Translate approved KPI and metric definitions into precise, testable, auditable transformation logic that matches agreed business meaning. • Define and enforce the promotion path from domain Gold to enterprise Gold, ensuring shared metrics are not published without required business and governance sign-off. • Partner directly with business stakeholders across domains to clarify KPI definitions, challenge ambiguity, and resolve competing definitions before implementation. • Enable domain teams by creating modeling standards, reusable design patterns, review processes, and coaching mechanisms rather than acting as the long-term owner of every downstream use case. • Review Gold-layer models created by other teams or partners for correctness, definition integrity, usability, and conformance to enterprise standards. • Optimize Gold-layer structures for BI and self-service analytics consumption while preserving traceability, governance, and metric consistency. • Ensure semantic models, tables, columns, ownership, and business definitions are documented and discoverable. • Apply awareness of data sensitivity, classification, and approved use when designing joins, dimensions, semantic views, and access patterns so the semantic layer reflects both business meaning and compliance requirements.