• Fazer parte de nossas squads multidisciplinares; • Participar do desenvolvimento de novas features; • Ajudar a manter nosso backlog de manutenção saudável; • Realizar testes de unidade (sempre contando com o apoio de nossos especialistas); • Aprender nossa regra de negócio e aplicá-la nas demandas do dia-a-dia;

Role Description The Senior Data Platform Engineer will support development and operation of a modern data environment supporting Navy financial management and audit artifact traceability initiatives. The role focuses on building and maintaining data pipelines, ingestion workflows, and transformation processes within Palantir Foundry and the Jupyter data platform environment. The engineer will work alongside ontology engineers and platforms lead to ingest, transform, and manage datasets that support artifact traceability, audit response reporting, and operational data applications. Key Responsibilities - Data Pipeline Development: - Develop and maintain Python-based data pipelines and transformation workflows within Palantir Foundry and the Jupyter platform. - Build ingestion pipelines integrating financial management, logistics, property, and other enterprise datasets. - Implement transformation logic to prepare raw datasets for curated data layers and ontology population. - Data Platform Operations: - Support dataset lifecycle management including refresh schedules, validation checks, and pipeline monitoring. - Troubleshoot pipeline failures and assist in maintaining platform data reliability and stability. - Maintain dataset lineage awareness and support platform data integrity practices. - Data Integration and Support: - Assist with integrating enterprise system data into the platform environment. - Collaborate with ontology engineers to ensure pipelines populate ontology objects and platform data structures correctly. - Document pipelines and transformation logic to support maintainability of the platform. Qualifications - Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or related technical field (or equivalent experience). - 4–7 years experience in data engineering or platform engineering roles. - Experience developing Python-based data pipelines. - Experience building ETL/ELT pipelines and data transformation workflows. - Experience working with enterprise data platforms such as Palantir Foundry, Databricks, AWS data platforms, or similar distributed data environments. - Experience integrating datasets from enterprise systems (ERP, financial systems, logistics systems, etc.) preferred. Requirements - Strong Python development skills for data processing and pipeline development. - Experience with SQL and data transformation frameworks. - Familiarity with distributed data platforms and large-scale data environments. - Strong troubleshooting and problem-solving skills in data pipeline operations. - Ability to collaborate with platform engineers, ontology engineers, and functional stakeholders. Benefits - Salary Range: $90-$115k per year.

• Support Pre-Sales Activities – Lead technical discovery sessions, scope operational requirements, deliver tailored solution demonstrations, and build impactful Proof of Concepts (PoCs) for prospects and clients. • Design Practical Contact Center Solutions – Translate client business challenges into technical designs leveraging Zendesk, Amazon Connect, and AWS, drawing on your practical knowledge of how these platforms function in real-world operations. • Develop Technical Documentation & Sales Collateral – Create solution architecture diagrams, technical proposal content, and functional configuration guides to support the sales cycle. • Collaborate with the Sales Team – Serve as the technical expert during client engagements, helping to clear technical hurdles and establish credibility with prospect IT and operations teams. • Contribute to Solution Best Practices – Align design choices with industry compliance, security, and governance standards, ensuring what is sold can be successfully deployed. • Support Feature & Trend Adoption – Stay current on AI, ML, and automation trends in contact centers, demonstrating to prospects how these features can be practically integrated into their workflows.

• Desenvolver e evoluir funcionalidades end-to-end (do refinamento à entrega em produção); • Trabalhar em conjunto com Produto/Design para entender necessidades e propor soluções técnicas viáveis; • Escrever código com foco em qualidade, legibilidade, testes e manutenção; • Participar de code reviews (recebendo e oferecendo feedback) e contribuir para padrões de codificação do time; • Colaborar com melhorias de arquitetura de código, refatorações e redução de débito técnico; • Criar e manter APIs, integrações e componentes reutilizáveis (de acordo com sua stack); • Ajudar a monitorar e sustentar sistemas em produção (observabilidade, correção de bugs, melhorias contínuas); • Contribuir para a construção de uma cultura de engenharia de software forte, colaborativa e orientada a resultados.