• Design, implement, and maintain ETL/ELT pipelines for structured and unstructured datasets from internal and external sources. • Build and optimize warehouses and marts (e.g., Snowflake, BigQuery) for analytics, reporting, and product use cases. • Ingest data from APIs and SaaS platforms (e.g., CRM, financial data APIs) into the core data platform. • Design conceptual, logical, and physical models to deliver scalable, consistent, high‑quality datasets. • Implement validation, schema management, and documentation to ensure accuracy and compliance. • Monitor and tune pipeline/warehouse performance for scalability and cost efficiency. • Apply data security and privacy controls aligned with financial regulations; ensure full transformation traceability. • Deliver clean, consistent datasets to analysts, PMs, and operations for fast, data‑driven decisions.

• Liderar o design e avaliar a arquitetura de dados a ser utilizada para solução de um problema. • Construir pipelines robustos, escaláveis e de alta disponibilidade. • Resolver incidentes críticos e atuar como referência técnica. • Aplicar os padrões, as boas práticas e as políticas definidas pela equipe de governança de dados. • Implementar processos de observabilidade (monitoramento, alertas, logging). • Orientar Engenheiros Júnior, Pleno e Senior; realizar code reviews. • Contribuir na estratégia e roadmap da plataforma de dados. • Colaboração e Desenvolvimento • Liderança técnica, visão estratégica e comunicação clara. • Capacidade de influenciar decisões e antecipar riscos. • Mentalidade de dono, foco em escalabilidade e resiliência.

• Design and optimize data pipelines and architectures for AI/ML workflows on cloud platforms. • Develop ETL/ELT processes, ensuring data quality, consistency, and accessibility. • Configure and manage AI/ML-ready cloud infrastructure (AWS Bedrock, Azure OpenAI). • Collaborate with Data Scientists to operationalize models and enable scalable AI solutions. • Implement data governance, versioning, and lineage tracking for compliance. • Monitor and maintain high-performance data systems and optimize for throughput.

• Contribute to the Snowflake migration: Work with the broader data team to deprecate our legacy Redshift and Apache Hop infrastructure by completing the migration to Snowflake and dbt Cloud • Build production-grade dbt pipelines: Develop and maintain SQL transformations in dbt that power analytics for business stakeholders across the organization • Establish data architecture: Refine and maintain our medallion architecture (bronze, silver, and gold layers) to create clear separation of concerns and a single source of truth • Collaborate with the data team: Partner with our AWS Engineer, Analytics Engineer, and Business Analyst in a sprint-based workflow with code reviews and regular standups • Maintain dbt transformations (60-70% of role): Own approximately 15-20 core models and summary views that drive business reporting, ensuring they're performant, well-documented, and easy to maintain • Build new data pipelines: Ingest data from APIs and new sources as business needs evolve • Enable reverse ETL: Develop and manage batch processes to send transformed data to downstream services like HubSpot using tools like Hightouch • Monitor data quality: Implement testing and alerting to catch issues before they impact stakeholders • Contribute to technical standards: Help establish and maintain best practices for code quality, documentation, and data modeling