• Design, build, and maintain reliable end-to-end ETL pipelines orchestrated with Apache Airflow • Integrate data from multiple sources (internal operational databases, third-party APIs, SaaS tools) into the Google Cloud Data Warehouse (BigQuery) • Design and evolve data models, warehouse schemas, and transformations to support scalable analytics and KPIs • Ensure data quality, reliability, and observability through monitoring, validation, and alerting • Own the product data structure, mapping product features and behaviors to analytics-ready data models • Define and maintain meaningful KPIs in collaboration with Product and BI • Enable analytics for AI-powered product features, ensuring visibility on usage, performance, quality, and business impact • Partner with Product, BI, and other stakeholders to gather requirements and deliver dashboards and reports • Maintain clear and up-to-date documentation for data models, pipelines, and metrics • Act as the primary bridge between Backend Engineering and BI, owning the flow from data production to analytics consumption • Triage, analyze, and address BI requests related to data availability, correctness, performance, and modeling • Collaborate with Backend Engineers on data contracts, schema evolution, and performance optimization, without owning core backend services • Proactively identify and resolve data-related issues impacting BI and Product teams • Own first-level monitoring and support for data pipelines and Airflow DAGs, ensuring timely resolution of failures • Collaborate with BI and Backend teams to troubleshoot and resolve complex issues • Continuously improve the stability, performance, and maintainability of the data platform

• Garantir a definição, governança e evolução dos dados mestre nos domínios críticos da companhia, atuando como referência arquitetural e viabilizando consistência sistêmica, eficiência operacional e tomada de decisão baseada em dados confiáveis. • Definir e evoluir a estratégia de Master Data Management (MDM) da companhia • Mapear e priorizar domínios críticos de dados mestre • Definir entidades, atributos, regras de negócio e fontes oficiais (systems of record) • Desenhar arquiteturas de fluxo de criação, atualização e consumo de dados mestre • Especificar padrões de integração e sincronização (event-driven, APIs, batch) • Definir e influenciar ownership de dados junto às áreas de negócio • Liderar ou direcionar iniciativas de implantação de MDM (ferramentas e processos) • Estabelecer e acompanhar métricas de qualidade de dados • Atuar na identificação de causas-raiz de problemas (não só sintomas) • Priorizar correções estruturais vs. saneamento manual • Ser referência para squads e arquiteturas na adoção do modelo de dados mestre • Atuar como elo entre negócio, dados e tecnologia

• Design, develop, and implement robust pipelines that connect data from diverse sources using primarily tools from the AWS ecosystem • Implement scalable and efficient data enrichment solutions using DBT to transform raw data into tailored data models and actionable business insights • Manage integrations, transformation processes, and task automation using data connectors, SQL APIs, Python, and REST • Use SQL to query and manipulate data in relational and non-relational databases • Apply Spark for processing large volumes of data in real time and in batch

• Design, develop, and implement robust data pipelines, integrating data from diverse sources primarily using tools from the AWS ecosystem; • Understand business and software product requirements and translate them into efficient, scalable data products; • Serve as a technical reference for the architectural design of data-driven and AI-first solutions, coordinating across product, analytics, and ML to define standards, best practices, SLAs, observability, and platform governance; • Implement scalable, efficient data enrichment solutions using DBT to transform raw data into tailored data models and actionable business insights; • Manage integrations, transformation processes, and task automation using data connectors, SQL APIs, Python, and REST; • Use SQL for querying and manipulating data in relational and non-relational databases; • Apply Spark for processing large volumes of data in real-time (Kafka and similar) and batch.