[Job-30246] Senior Business Intelligence Analyst/ Engineer, Brazil

At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.

With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.

We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.

CI&T is strengthening its data practice to support a key client in the Logistics sector, where data-driven decision-making is rapidly becoming a critical competitive advantage.

We are looking for a Senior BI Analyst / Engineer to join the Logistics Platform team  a professional who bridges the gap between raw data and meaningful business insight, building the analytical foundations that empower teams to act with confidence and clarity.

In this role, you will be responsible for designing and maintaining analytical models and dashboards, developing advanced SQL and Python-based solutions, and actively fostering a data-driven culture across application and business teams. The ideal candidate is a self-driven, analytically sharp professional who combines technical depth with strong communication skills  capable of independently engaging stakeholders onsite and offshore, translating complex business requirements into reliable, scalable analytical solutions.

• →Analytics & Visualization: Design, build, and maintain analytical models and dashboards in Power BI and Tableau, delivering clear and actionable insights on operational performance and business KPIs.
• →Data Analysis: Analyze data to identify trends, patterns, and improvement opportunities, transforming findings into concrete recommendations that support strategic and operational decision-making.
• →SQL & Scripting: Develop complex SQL queries , including advanced use of regex and leverage Python for scripting, automation, and data processing workflows that support analytical models.
• →Data Engineering & Modeling: Adopt and promote best practices in data engineering, data modeling, and data integration, ensuring the reliability, scalability, and performance of analytical solutions end-to-end.
• →Stakeholder Enablement: Guide application and business teams on data interpretation and usage, acting as a trusted analytical reference and actively contributing to building a data-driven decision-making culture within the team.
• →Requirements & Validation: Independently engage with business and technology teams to gather, clarify, and validate analytical requirements, ensuring solutions are aligned with real business needs.
• →Agile Collaboration: Manage tasks end-to-end within Agile/Scrum environments using tools such as Jira, effectively prioritizing deliverables and resolving issues across distributed onsite and offshore teams.

• Advanced English (C1 or above)  reading, writing, and spoken communication with onsite and offshore stakeholders independently
• IT experience, with experience in Business Intelligence, Data Engineering, or Analytics
• Proven experience building and maintaining analytical models and dashboards in Power BI and Tableau
• Advanced proficiency in SQL, including complex queries and regex usage
• Strong command of Python for scripting and data process automation
• Solid analytical skills to understand data, interpret business requirements, and translate them into actionable analytical solutions
• Experience with Agile/Scrum methodologies and project management tools such as Jira
• Strong prioritization, problem-solving, and end-to-end task management skills in dynamic, distributed team environments

• Knowledge of the Azure platform , particularly Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and Azure Data Lake
• Broader familiarity with Azure cloud services and cloud-based data architecture patterns
• Experience working within the Logistics or Supply Chain domain
• Exposure to data governance and data quality practices in analytical environments