• Lead and participate in discovery conversations to understand client goals, constraints, and current-state data environments • Guide clients through data platform, architecture, and tooling decisions, clearly explaining options and tradeoffs • Design and deliver technical roadmaps that support scalable data and analytics platform adoption • Apply strong data modeling and engineering best practices to support analytics, reporting, AI, and machine learning use cases • Stay current on modern data engineering trends, cloud platforms, and emerging technologies • Design and build modern, reliable data pipelines and ETL/ELT frameworks • Architect cloud-native data infrastructure with a focus on scalability, maintainability, and data quality • Integrate and unify data from diverse sources including SQL databases, APIs, spreadsheets, and cloud storage • Own technical direction for client engagements, balancing best practices with real-world constraints • Collaborate closely with analysts, consultants, and client stakeholders to translate business needs into technical solutions • Deliver billable consulting services, including hands-on engineering work and client advisory engagements

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description - Desenvolver e operacionalizar modelos de Machine Learning, garantindo a implementação e manutenção de pipelines eficientes. - Realizar deploy de modelos e gerenciar o ciclo de vida de modelos (LCM) dentro da plataforma GenPlat. - Colaborar com a equipe para fomentar uma cultura forte de IA e MLOps, promovendo práticas de inovação e melhoria contínua. - Apoiar a implementação de agentes e ferramentas baseadas em GenAI, contribuindo para a estabilidade técnica e organizacional. - Participar ativamente no planejamento e execução de projetos, garantindo alinhamento com os objetivos estratégicos da empresa. Qualifications - Experiência em MLOps e deploy de modelos, com sólida base em engenharia de software. - Conhecimento em infraestrutura em nuvem, preferencialmente AWS, e operação de pipelines. - Familiaridade com soluções open-source e vivência em ambientes tech-first. - Capacidade de comunicação clara e colaboração eficaz em equipes multidisciplinares. - Mentalidade voltada para inovação e adaptação a mudanças rápidas. Requirements - Conhecimentos em GenAI e sua aplicação prática em projetos. - Habilidade para liderar iniciativas técnicas e fomentar um ambiente de aprendizado contínuo. - Experiência em aumentar a eficiência no gerenciamento do ciclo de vida de modelos. - Capacidade de se adaptar rapidamente a novas tecnologias e desafios complexos. - Inglês avançado. Company Description Somos uma empresa brasileira de tecnologia referência na América Latina. Por meio de soluções inovadoras, conectamos milhares de restaurantes a milhões de consumidores diariamente com uma média de 100 milhões de pedidos mensais. Além do delivery de comida, também somos Mercado, Farmácia e Pet. Temos também o iFood Pago, nossa Fintech, que engloba o iFood Benefícios, o vale alimentação e refeição do iFood e o próprio iFood Pago, o banco do restaurante.

• Design, build, and operate scalable batch and streaming data pipelines • Develop and orchestrate workflows using Apache Airflow • Implement transformations and analytics-ready datasets using DBT • Build and maintain real-time pipelines using Kafka • Leverage Databricks for data processing, analytics, and AI enablement • Support AI and GenAI use cases, including enabling high-quality data access for tools like Databricks Genie • Design and optimize data storage using Apache Iceberg and Lakehouse architecture • Ingest and manage data from diverse internal and external sources using Fivetran • Handle a wide variety of data structures (structured, semi-structured, and event-based data) • Build and maintain a semantic layer that enables trusted reporting and self-service analytics • Implement data quality frameworks, monitoring, and unit test automation to ensure reliability at scale • Partner with BI, product, and engineering teams to deliver data that is intuitive, trusted, and actionable • Optimize performance, scalability, and cost across AWS services such as Athena, EC2, and related tooling • Contribute to data platform standards, documentation, and best practices

• Develop, construct, test, and maintain data architectures, including databases and large-scale processing systems. • Design, build, and optimize data pipelines and ETL/ELT processes leveraging Snowflake and Azure Services. • Develop and maintain Snowflake data warehouses, ensuring efficient data modeling, partitioning, and performance tuning. • Implement data flow processes that automate and streamline data collection, processing, and analysis. • Ensure data governance, quality, and security best practices across all data platforms. • Collaborate with analytics and business teams to improve data models that feed business intelligence tools, increasing data accessibility and fostering data-driven decisionmaking across the organization. • Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. • Provide operational support for existing data infrastructure and develop new solutions as needed. • Monitor, troubleshoot, and optimize system performance in Azure and Snowflake environments. • Support CI/CD pipelines and automation for data workflows and deployments. • Keep current with industry trends and innovations in data engineering and propose changes to the existing landscape.