October 2025 performance summary for prefeitura-rio/queries-rj-sms: Delivered comprehensive data platform enhancements across MongoDB pipelines, SER data models, cancer surveillance data mart, and canonical patient dimensions, plus development hygiene fixes. These efforts increased data timeliness, accuracy, and maintainability, enabling faster analytics for health programs and improved data governance.

September 2025 monthly summary for prefeitura-rio/queries-rj-sms: Delivered significant data-model and ingestion improvements to SISREG domain, enabling faster, richer analytics and more reliable data for operations and reporting. Key features include: SISREG Solicitacoes and Marcacoes Data Model Enhancements with consolidated incremental model and improved partitioning; SISREG Procedures Data Model Enhancements with latest-record selection and contextual grouping; SISREG API Logs Ingestion with incremental materialization, deduplication, and enhanced lookback handling. Major issues fixed include JSON parsing syntax error in laudo field and stabilization of incremental logic across API logs. Business impact: near real-time data availability, reduced ETL runtime, and richer clinical/procedural context for decision support. Technologies/skills demonstrated: SQL data modeling, partitioning, incremental materialization, ETL design, deduplication, lookback handling, and data context enrichment.

In August 2025, delivered key performance, reliability, and data-modeling improvements for prefeitura-rio/queries-rj-sms. Completed SISREG API performance and data integrity enhancements including query optimizations, refined partitioning, unique constraints, logging for data extraction runs, and a new log model configuration; introduced SISREG procedures data model and dedup fixes. Fixed SISREG deduplication to ensure only the most recent record per solicitacao_id. Expanded data modeling and dataset preparation for Coppe Hackathon including equipment, habilitations, beds, professionals, regulation data, and wait times with improved naming. Implemented SISCAN data model for mammography exam results with quality tests, improved parsing, and optimized web laudos processing. Initiated CNS/SIA data integration with fuzzy matching for patient linkage and adjusted materialization strategies to support data quality and analytics. These efforts reduce latency, prevent duplicates, widen data asset coverage, and enable more accurate analytics for healthcare and municipal services.

July 2025: Delivered substantive improvements across two Rio health data pipelines, emphasizing robustness, performance, and data integrity. Implemented a comprehensive SISREG data model overhaul and schema alignment, standardized date handling and user data pipelines for analytics readiness, and maintained dependency integrity. Result: higher data quality, faster ingestion, clearer error visibility, and scalable foundations for RegulAi integrations.

June 2025 performance summary: Two focused deliverables across queries-rj-sms and pipelines_rj_sms that improve data quality, reliability, and processing efficiency. Key outcomes include: (1) AP mapping reliability enhancement in dim_estabelecimento_bairro_ap by prioritizing id_distrito_sanitario with neighborhood normalization, delivering more accurate Area Programatica (AP) assignments. (2) Data Lake ingestion robustness: implemented paginated MongoDB extraction and slice-based uploads to the data lake, and migrated output to Parquet format for faster downstream analytics and better compatibility. Impact: higher data accuracy for AP mappings, reduced memory risk and faster processing for large datasets, enabling scalable reporting and analytics. Technologies/skills demonstrated: SQL refactoring, data normalization, ETL best practices, paginated extraction, Parquet data format, MongoDB data ingestion, and data lake workflows.