• design, build, and maintain well-architected ETL pipelines • work at enterprise scale and process terabytes of financial data • ensure data is timely, accurate, and stored within contractually required timelines • build and maintain ingestion pipelines using AWS Glue • process and ingest large-volume XML filings • store curated zone data in Apache Iceberg tables • prevent duplicate data loads across target destinations • integrate the agency's ETL Common Library into Glue jobs • ensure all ETL jobs populate ETL Load Reports in real-time • implement technical data quality standards • develop and maintain the semantic layer • deploy ETL resources using CloudFormation templates • create and maintain documentation suite for each dataset • support operationalization of statistical outputs and derived data products

• Develop new ETL pipelines and data ingestion processes alongside senior engineers using AWS Glue (Spark-based, PySpark), MWAA (Airflow), Lambda, and SNS • Integrate the agency's ETL Common Library into Glue jobs for standardized orchestration, error handling, metadata recording, and SNS notifications for all success and error job events • Ingest structured and semi-structured datasets (CSV, XML, JSON, Avro, pipe-delimited) into S3 landing, raw, and curated zones using Apache Iceberg tables • Configure static ETL metadata in the centralized PostgreSQL metadata store; ensure dynamic metadata records job status and timestamps for all key execution steps • Monitor assigned production jobs and participate in operations support rotations • Ensure ETL Load Reports are populated in real-time and ETL Gap Reports are updated on a weekly basis • Build and maintain materialized views and semantic layer objects in Trino and Athena to ensure optimized query performance and consistent business logic • Produce and maintain required documentation for each assigned dataset: Business Requirements, ETL Design Documents, Data Models, Data Dictionaries, Mapping Documents, Deployment Documents, O&M Guides, and ETL Test Plans • Write unit and integration tests achieving the 90% minimum code coverage threshold; complete security scans at least once per sprint • Deploy ETL resources using CloudFormation templates through the agency CICD pipeline • Support transition of ETL jobs from other agency teams and disaster recovery exercises

Role Description A Vitru Educação está em busca de professor(a)-autor(a) para atuar na revisão técnica, validação acadêmica e curadoria conceitual da disciplina POWER QUERY E ETL (M LANGUAGE), do curso Power BI e Data Visualization Avançada (Storytelling e DAX), na modalidade pós-graduação EAD. SOBRE A ATIVIDADE: - Curadoria e validação de conteúdo - Revisão técnica de 3 temas (20 a 30 laudas cada) - Análise de atualidade conceitual, linguagem e adequação ao público - Conferência e eventual complementação de referências - Realização de ajustes diretamente no material (não apenas sugestões) - Atividades avaliativas e complementares - Validação de 3 questões de autoestudo por tema - Revisão e validação de 15 questões objetivas por tema - Validação de materiais complementares (1 estudo de caso + 10 questões) - Validação de roteiros e gravação de 3 videoaulas (10 a 15 minutos cada) EMENTA DA DISCIPLINA: - Conectores de dados - Transformação e limpeza de dados com Power Query - Linguagem M - Parâmetros e funções personalizadas Qualifications - Pós-graduação lato ou stricto sensu – Especialização, Mestrado ou Doutorado na área da disciplina* - Familiaridade com Google Docs e/ou Word - Organização, gestão de prazos e clareza na escrita - Experiência prévia na produção de livros ou materiais educacionais será considerada um diferencial - Power BI e Data Visualization Avançada (Storytelling e DAX) Benefits - Atividade 100% home office - Acompanhamento especializado durante todas as etapas - Publicações com registro de ISBN - Desenvolvimento de competências autorais digitais - Visibilidade nacional e reconhecimento acadêmico Company Description A Vitru valoriza e apoia a diversidade em todas as suas formas. Todas as candidaturas são bem-vindas.

• Build & Enhance Pipelines: Design, develop, and maintain scalable data pipelines to ingest, transform, and enrich complex healthcare data using Databricks and Spark. • Optimize Data Workflows: Analyze and improve data intake processes and optimize SparkSQL/Python workloads for performance, scalability, reliability, and cost efficiency. • Design Data Models: Develop and maintain data marts, semantic models, and curated datasets that support analytics products, reporting, and business intelligence initiatives. • Ensure Data Quality & Reliability: Monitor pipeline health, troubleshoot production issues, implement data validation frameworks, and maintain high standards for data quality and governance. • Collaborate Across Teams: Partner with product, analytics, and engineering teams to understand business requirements and deliver scalable data solutions. • Drive Continuous Improvement: Contribute to architecture decisions, engineering standards, automation efforts, and best practices across the data platform.