• Projetar, desenvolver e manter pipelines de dados e transformações no Palantir Foundry (PySpark, SQL, Code Workbook). • Construir e gerenciar ontologias, tipos de objetos e relacionamentos dentro da camada de Ontologia do Foundry. • Implementar e suportar fluxos de trabalho e agentes de IA/ML usando Palantir AIP. • Colaborar com partes interessadas do negócio para traduzir requisitos em soluções de dados escaláveis. • Garantir a qualidade dos dados, governança e melhores práticas de segurança em todos os pipelines. • Criar e manter dashboards e aplicações de dados dentro do Foundry Workshop. • Fornecer orientações técnicas e documentação para decisões de arquitetura de dados.

• Design, develop, and maintain scalable data ingestion, transformation, and publishing pipelines utilizing Databricks and AWS services. • Implement and optimize Databricks Lakehouse capabilities including Unity Catalog, Delta Live Tables, Auto Loader, Databricks SQL, and Delta Sharing. • Build and maintain governed data products supporting operational, analytical, reporting, and machine learning workloads. • Develop and support medallion architecture data pipelines and enterprise data quality frameworks. • Implement data governance controls, metadata management, lineage tracking, and data retention policies. • Collaborate with cloud engineers, architects, cybersecurity specialists, and business stakeholders to deliver secure, production-ready solutions. • Optimize platform performance through partitioning, clustering, caching, workload tuning, and query optimization techniques. • Support analytics enablement through semantic layers, dashboards, reporting solutions, and self-service data access capabilities. • Participate in architecture reviews, operational readiness activities, platform modernization initiatives, and continuous improvement efforts. • Create and maintain technical documentation, design artifacts, operational procedures, and engineering standards.

• Data Ingestion & ETL: Build Python/SQL pipelines to ingest invoices, orders, and catalogs. • Perform historical backfills via SFTP/API and manage Airflow DAGs. • Instance Configuration: Set up custom fields, product filtering logic, and sales workflows. • Manage SSO and user provisioning for new rollouts. • AI-Augmented Engineering: Leverage AI coding assistants (Copilot, Cursor) and LLMs to accelerate Python/SQL script generation, data mapping, and debugging. • Customer Communication & Projects: Act as a technical point of contact. Translate complex data issues into clear updates for customers. Own project milestones from kickoff to "Go Live." • Integration & Automation: Build Workato recipes and connect customer ERPs via APIs/webhooks to ensure real-time data flow. • QA & Troubleshooting: Triage HubSpot support tickets, debug data discrepancies in large data sets, and deploy production fixes. • Documentation: Maintain customer data mappings and internal technical runbooks.

• Architect and optimize large-scale data platforms on Google Cloud, with BigQuery as the analytical backbone • Design and build unified batch and streaming pipelines that handle high-volume, mission-critical workloads • Lead infrastructure-as-code practices, ensuring environments are repeatable, secure, and version-controlled • Implement open table formats to enable cross-cloud and cross-engine data interoperability • Establish automated data quality, metadata, and lineage practices across the data estate • Partner with data scientists, analysts, and product teams to translate business needs into reliable data products • Mentor engineers, review designs, and raise the bar on engineering standards