• Influence technical strategy: Define and drive the long-term technical roadmap for Affirm’s Lakehouse Platform across Apache Iceberg, Spark, Snowflake, and cloud-native storage, balancing scalability, reliability, governance, performance, and cost. • Design and develop: Architect and implement platform capabilities that make analytical data secure, trustworthy, discoverable, and easy to use across Affirm’s engineering, analytics, machine learning, and business teams. • Strengthen governance and access controls: Design and operate secure, auditable data access capabilities across Snowflake and the lakehouse platform, including RBAC, dynamic data masking, cataloging, lineage, classification, and privacy policy enforcement. • Improve analytics engineering foundations: Partner with Analytics Engineering to evolve data modeling, transformation pipelines, testing frameworks, documentation standards, and data quality practices that enable trustworthy self-service analytics. • Operate at scale: Establish best practices for lakehouse operations, including schema evolution, table maintenance, partitioning, compaction, observability, incident response, production support, and readiness for on-call operations. • Optimize performance and cost: Identify and execute improvements across analytical compute and storage, including Snowflake warehouse tuning, query optimization, storage layout, lifecycle management, cost attribution, and operational efficiency. • Collaborate cross-functionally: Partner with Infrastructure, Lakehouse Analytics, Analytics Engineering, Machine Learning, BI, Product Engineering, and SRE to translate stakeholder needs into durable platform architecture. • Innovate: Stay ahead of industry trends in lakehouse architecture, open table formats, analytical compute engines, data governance, privacy engineering, semantic layers, agentic data tools, and AI-ready data infrastructure. • Build teams: Mentor engineers, raise technical quality, and foster an inclusive culture of design rigor, operational excellence, and continuous learning.

• Influence technical strategy: Define and drive the long-term technical roadmap for Affirm’s Lakehouse Platform across Apache Iceberg, Spark, Snowflake, and cloud-native storage, balancing scalability, reliability, governance, performance, and cost. • Design and develop: Architect and implement platform capabilities that make analytical data secure, trustworthy, discoverable, and easy to use across Affirm’s engineering, analytics, machine learning, and business teams. • Strengthen governance and access controls: Design and operate secure, auditable data access capabilities across Snowflake and the lakehouse platform, including RBAC, dynamic data masking, cataloging, lineage, classification, and privacy policy enforcement. • Improve analytics engineering foundations: Partner with Analytics Engineering to evolve data modeling, transformation pipelines, testing frameworks, documentation standards, and data quality practices that enable trustworthy self-service analytics. • Operate at scale: Establish best practices for lakehouse operations, including schema evolution, table maintenance, partitioning, compaction, observability, incident response, production support, and readiness for on-call operations. • Optimize performance and cost: Identify and execute improvements across analytical compute and storage, including Snowflake warehouse tuning, query optimization, storage layout, lifecycle management, cost attribution, and operational efficiency. • Collaborate cross-functionally: Partner with Infrastructure, Lakehouse Analytics, Analytics Engineering, Machine Learning, BI, Product Engineering, and SRE to translate stakeholder needs into durable platform architecture. • Innovate: Stay ahead of industry trends in lakehouse architecture, open table formats, analytical compute engines, data governance, privacy engineering, semantic layers, agentic data tools, and AI-ready data infrastructure. • Build teams: Mentor engineers, raise technical quality, and foster an inclusive culture of design rigor, operational excellence, and continuous learning.

• Apoiar a construção e manutenção (processo e modelagem completa) do nosso data lake nas camadas silver e gold. • Apoiar a construir e manter pipelines de dados robustos e escaláveis. • Automatizar processos de coleta, transformação e integrações de dados. • Garantir a qualidade, consistência e governança completa dos dados. • Trabalhar em parceria com analista, cientista de dados, engenheiro e stakeholders de produto. • Realizar refinamento, documentação e catalogação de projetos e métricas. • Apoiar ativamente as áreas de negócio com dados confiáveis para tomada de decisão. • Atuar e construir ativamente a partir da camada gold de dados dashboards estratégicos.

• Design, implement, and operationalize data governance frameworks across client engagements, covering the following areas: • Define and implement data governance frameworks, policies, and standards tailored to client environments. • Establish data ownership, stewardship models, and accountability structures across business and technical teams. • Develop and maintain data dictionaries, business glossaries, and classification taxonomies. • Ensure compliance with data regulations and internal data management policies (GDPR, BCBS 239, etc.). • Design and implement data quality rules, profiling routines, and monitoring dashboards. • Investigate and remediate data quality issues across structured and unstructured data sources. • Map and document end-to-end data lineage to support auditability and impact analysis. • Define and track data quality KPIs and SLAs in collaboration with data owners. • Deploy and administer data catalog and metadata management tools (e.g. Informatica, Collibra, Alation, Microsoft Purview). • Enrich metadata assets with business context, ownership, sensitivity classification, and usage information. • Drive adoption of the data catalog across business and technical teams. • Configure and operate data governance platforms, primarily Informatica (IDMC, Axon, EDC, DQ) and equivalent tools. • Integrate governance tooling with existing data pipelines, warehouses, and BI environments. • Automate data quality checks and governance workflows within ETL/ELT pipelines. • Provide internal training and knowledge-sharing sessions on data governance best practices. • Support the Team Lead/Manager on client relationships, business development, and internal projects.