
Rafael Aguilar developed and enhanced data ingestion and transformation pipelines within the edanalytics/earthmover_edfi_bundles and edu_edfi_source repositories, focusing on Ed-Fi-aligned assessment data. He engineered ETL processes and SQL macros to standardize and process NWEA MAP Fluency and South Carolina EOCEP assessment data, using Python, SQL, and dbt for configuration management and data modeling. Rafael addressed schema alignment by refactoring data structures and implemented flexible, schema-driven extraction for credential extensions. His work improved data quality, interoperability, and reporting readiness, demonstrating depth in data engineering and transformation while reducing manual intervention and supporting scalable onboarding of new assessment and credential schemas.

Month 2025-10 highlights for edanalytics/edu_edfi_source: delivered an extended extract_extension macro with predefined TPDM/EPDM extensions, updated configurations, and merged predefined and user-defined extensions to enable flexible, schema-driven data extraction. This work enhances data extraction reliability, reduces manual configuration for new data sources, and supports scalable onboarding of credential schemas.
Month 2025-10 highlights for edanalytics/edu_edfi_source: delivered an extended extract_extension macro with predefined TPDM/EPDM extensions, updated configurations, and merged predefined and user-defined extensions to enable flexible, schema-driven data extraction. This work enhances data extraction reliability, reduces manual configuration for new data sources, and supports scalable onboarding of credential schemas.
July 2025 monthly summary for edanalytics/earthmover_edfi_bundles focused on delivering two high-impact features and strengthening data processing for state assessments.
July 2025 monthly summary for edanalytics/earthmover_edfi_bundles focused on delivering two high-impact features and strengthening data processing for state assessments.
January 2025 — Focused, data-model centric improvement in the earthmover_edfi_bundles repo to strengthen MAP Fluency assessment processing. Implemented a data-structure fix for academicSubjects by switching from a string array to an array of objects with academicSubjectDescriptor, ensuring accurate downstream interpretation in the assessment pipeline and aligning with ED-Fi bundle standards. Completed as part of ongoing data integrity and interoperability enhancements across the ED-Fi bundles, contributing to more reliable analytics and reporting.
January 2025 — Focused, data-model centric improvement in the earthmover_edfi_bundles repo to strengthen MAP Fluency assessment processing. Implemented a data-structure fix for academicSubjects by switching from a string array to an array of objects with academicSubjectDescriptor, ensuring accurate downstream interpretation in the assessment pipeline and aligning with ED-Fi bundle standards. Completed as part of ongoing data integrity and interoperability enhancements across the ED-Fi bundles, contributing to more reliable analytics and reporting.
Concise monthly summary for 2024-11 focusing on highlights in edanalytics/earthmover_edfi_bundles. Delivered a data ingestion/processing bundle that enables ingesting and transforming NWEA MAP Fluency assessment data into Ed-Fi format, including configuration, seeds, and templates for foundational skills, adaptive oral reading, and dyslexia screeners with performance levels and scores. No major bugs reported this month. This work lays the foundation for standardized reporting, improved data quality, and accelerated downstream analytics across MAP Fluency data.
Concise monthly summary for 2024-11 focusing on highlights in edanalytics/earthmover_edfi_bundles. Delivered a data ingestion/processing bundle that enables ingesting and transforming NWEA MAP Fluency assessment data into Ed-Fi format, including configuration, seeds, and templates for foundational skills, adaptive oral reading, and dyslexia screeners with performance levels and scores. No major bugs reported this month. This work lays the foundation for standardized reporting, improved data quality, and accelerated downstream analytics across MAP Fluency data.
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