• Participates in planning, definition, and high-level design of data solutions, exploring alternatives and evaluating new technologies. • Develops and maintains scalable cloud-based data infrastructure, ensuring alignment with the organization's decentralized data management strategy. • Designs and implements ETL pipelines using AWS services (e.g., S3, Redshift, Glue, Lake Formation, Lambda) to support data domain requirements and self-service analytics. • Collaborates with data domain teams to design and deploy domain-specific data products, adhering to organizational standards for schema design, data transformations, and storage solutions. • Establishes and enforces data governance practices, including compliance with data privacy, access controls, and lineage requirements, across the organization’s data assets. • Maintains a comprehensive data catalog and governance tools, automating workflows to uphold data integrity and ensure discoverability across all domains. • Leads the implementation and ongoing support of data mesh architecture, ensuring seamless integration and data flow across multiple domains. • Ensures the availability and reliability of the organization’s Data Mesh software, managing operations and maintenance of the framework. • Provides guidance and leadership on the cultural adoption of data mesh principles, including organizational training initiatives for data stewards and users. • Communicates effectively with stakeholders to align technical data architecture with business goals, ensuring long-term support for analytics, insights, and innovation. • Experience working in an Agile organization using Scrum, Kanban, Jira, Confluence, and SAFe. • Provide team specific training as needed.

• Design and implement scalable data models optimized for AI/ML use cases, ensuring data is structured for effective model training and inference • Architect and manage data pipelines using Databricks, including orchestration, job scheduling, and workflow optimization • Develop and maintain robust ETL/ELT processes to support data ingestion, transformation, and delivery across systems • Leverage Databricks Asset Bundles to manage deployment of data assets (pipelines, jobs, notebooks, and files) across environments • Collaborate with cross-functional teams to align data architecture with AI initiative requirements and business objectives • Ensure data quality, integrity, and governance standards are met across all pipelines and datasets • Contribute to CI/CD practices using Azure DevOps (ADO), with a future transition to GitHub-based workflows • Participate in Agile development processes, including sprint planning, stand-ups, and iterative delivery • Other duties as assigned

• Atue como Engenheiro de Dados Sênior DataStage em projetos estratégicos. • Liderando soluções de dados de alta complexidade em uma empresa de grande porte. • Na Selbetti, você será protagonista na construção de pipelines de dados robustos. • Apoiando decisões críticas de negócio e interagindo diretamente com clientes e times internacionais. • Liderar o levantamento de requisitos de dados junto ao cliente. • Realizar mapeamento, modelagem e integração de dados em DataStage. • Desenhar, desenvolver e otimizar jobs ETL complexos. • Conduzir testes, homologação e validação técnica com o cliente. • Garantir qualidade, performance e governança dos fluxos de dados. • Fazer o microgerenciamento das demandas e pendências do projeto. • Colaborar com equipes estrangeiras em inglês ou espanhol.

The projectYou'll work with our client, an American private equity and investment management firm – Fortune 500, based in New York. We support them in the area of infrastructure and data platform, and we build AI systems within that environment. The client works across finance, credit, investments, and real estate. Snowflake is the central data warehouse. Much of your work will start with data that already lives there and end with an AI-powered system the client's teams rely on. What you'll do - Design and ship LLM-powered applications: prompt pipelines, RAG, retrieval, evaluation. From validated proof of concept to production. - Deliver MCP servers that expose data and services to AI agents and applications. - Implement the security and authentication layer around them: enterprise authentication, custom IdP integrations, OAuth/OIDC flows. - Consume external APIs and wrap them into services – web services, traditional APIs, or basic UIs. - Integrate LLM providers (OpenAI and others) into client systems. - Build semantic search and retrieval on vector databases (Pinecone). - Query, process, and analyze data in Snowflake using SQL and Python. Prepare datasets that feed AI features and analytics. - Contribute to data pipelines when needed. Other team members own them day to day, so this stays occasional. - Document design decisions before implementation.