Role Description We’re looking for a Vice President, Data to build, lead, and grow R&P’s data practice from the ground up. This is an important role at Robots & Pencils, a player-coach position that demands equal parts technical depth, business ownership, and leadership. You will establish R&P as a go-to partner for: - Data strategy - Modern data architecture - AI-ready platforms - Business intelligence - Data engineering You will own the practice as a true business, with full accountability for: - Revenue - Profitability - Client outcomes - Team development You’ll report to the Global Head of Delivery and be part of the Senior Leadership team. Qualifications - 10+ years of progressive experience in data and analytics - At least 3 years at director, principal, or practice-lead level within a professional services or technology consulting business - Demonstrated track record of building or scaling a data practice or service line - Proven experience solutioning and delivering complex data engagements - Deep expertise in Databricks, Snowflake, and/or AWS (Redshift/S3) - Comprehensive command of the data ecosystem - Expertise across the full data value chain - Strong executive presence - History of developing and mentoring high-performing technical teams Requirements - Act as the senior data authority in front of client CTOs, CIOs, and heads of data - Develop strategic views on where the data market is headed - Champion internal and external thought leadership - Develop best-in-class solutions architecture recommendations - Contribute to R&P's AI software development lifecycle - Build and lead a high-performing team of data consultants, engineers, BI leads, and data scientists - Set the technical standards, career architecture, and development pathways for data practitioners - Cultivate a collaborative, inclusive practice culture - Build the data practice into a $5m revenue practice in the first twelve months - Set a clear vision for the practice and communicate it compellingly - Define service lines, target industries, pricing logic, and operating model - Partner with the sales organization and client teams - Lead and support presales solutioning and scoping engagements - Lead the firm’s Databricks relationship and adjacent data partnerships Benefits - Comprehensive package of benefits including a bonus program - Paid time off - Medical/dental/vision insurance - 401(k) to eligible employees

• Develop and maintain scalable data pipelines to support business requirements. • Collaborate with cross-functional teams to gather and analyze data requirements. • Implement data integration solutions using ETL tools and technologies. • Optimize and improve data processing workflows to enhance performance. • Ensure data quality and integrity through rigorous testing and validation.

Role Description Como Senior Data Engineer, você atuará na evolução da nossa plataforma de dados, desenvolvendo soluções escaláveis em um ambiente de alta volumetria e colaborando com diferentes times para transformar dados em valor para o negócio. O seu dia a dia no Skeelo vai ser assim: - Desenvolver e evoluir pipelines de ETL para ingestão, transformação e disponibilização de dados. - Construir integrações entre diferentes fontes de dados, garantindo qualidade, performance e padronização. - Desenvolver soluções em Python e Spark, atuando na cloud da AWS sob a plataforma Databricks. - Monitorar e evoluir pipelines em produção, garantindo confiabilidade e eficiência dos processos. - Trabalhar em conjunto com os times de Analytics, Produto e Engenharia para atender novas demandas de dados. - Garantir que os dados estejam disponíveis com alta qualidade e baixa latência para consumo das áreas da empresa. - Contribuir com melhorias na arquitetura, nas boas práticas e na evolução contínua da engenharia de dados. Qualifications - Experiência sólida como Data Engineer, desenvolvendo e sustentando pipelines de dados em produção. - Experiência com AWS, Python, Databricks e processos de ETL batch e streaming. - Conhecimento em bancos de dados relacionais e não relacionais, além de ferramentas como Kafka, Kibana e Elasticsearch. - Boa comunicação para colaborar com diferentes áreas e traduzir necessidades de negócio em soluções técnicas. - Inglês para leitura e compreensão de documentações técnicas. Requirements - Experiência em empresas de tecnologia ou fintechs de alta escala. - Vivência com Data Lakes e arquiteturas modernas de dados. - Experiência em ambientes com grande volume de dados. - Facilidade para documentar soluções e compartilhar conhecimento com o time. Benefits - Plano de saúde (sem custo e sem coparticipação para você e seus familiares) + odonto e seguro de vida. - VA/VR flexível no Caju (liberdade real de uso). - Bem-estar completo: Totalpass (atividade física) + ZenKlub (saúde mental). - PPR anual atrelado a metas e impacto. - Acesso ao Skeelo Premium + clube do livro com ebook do mês. - Auxílio creche + licença parental estendida. - Parceria com escolas de idiomas, Berlitz e Open English. - Feriados de SP emendados + recesso remunerado no fim do ano. - Day off de aniversário.

• Data Science & Applied ML: Research, prototype, and develop ML and LLM-based models to solve complex business problems, with a current focus on risk detection and prioritization • Wrap models into production-ready APIs and integrate them into our core product • Ensure model outputs are interpretable — translating predictions into actionable reason codes for end users • Partner directly with operational teams to gather feedback, refine features, and improve model relevance over time • Data Engineering: Design, build, and maintain scalable pipelines to ingest data from disparate sources into our data warehouse/lake • Implement robust data validation, quality checks, and transformation workflows across raw, curated, and serving layers • Build and maintain curated datasets optimized for both analytics and model training use cases • MLOps & Production Ownership: Implement and maintain CI/CD pipelines for both data workflows and ML model deployment across environments • Monitor pipeline latency, data drift, and model performance in production; design alerting and retraining triggers • Own the business outcomes of your models — define success metrics, track ROI, and iterate based on real-world efficacy • Manage infrastructure as code and containerized deployments to ensure reproducible, environment-consistent releases