General Manager of Data, Artificial Intelligence, and Automation

Azul is looking for a General Manager to consolidate Data, Artificial Intelligence, and Automation under a single executive leadership. The position is responsible for three fronts with a direct impact on results: a data platform that supports commercial, operational, and financial decisions; a portfolio of generative and predictive AI, which already influences pricing, maintenance, commercial, and passenger experience; and an automation pipeline that removes manual work from daily operations. This person will manage a multidisciplinary team, will have a seat in leadership discussions, and will be responsible for the ROI, governance, and risk of each of the three fronts. This is not a technical back-office position: it is a position that helps decide, alongside the company's strategic areas, where Azul bets on technology to grow.

Responsibilities and assignments

• Lead the transition of the data architecture from the pipeline-to-BI model to a model oriented toward data products, where AI agents and Data Scientists consume data with semantic context, guaranteed quality, and defined contracts.
• Define and execute Azul's data strategy, covering Data Engineering and Data Science, with Snowflake as the central platform.
• Ensure availability, quality, security, governance, and cost management (FinOps) of the data platform.
• Connect the data strategy to business growth, acting as a partner to areas such as Commercial, Operations, Maintenance, Logistics, Loyalty, and Finance.
• Manage and develop a multidisciplinary team of data engineers, data scientists, and analysts.
• Integrate the Data front with other IT areas — Architecture, Business Partners, and Information Security.
• Ensure compliance of data practices with LGPD, including classification, retention, and processing of personal data.

Artificial Intelligence

• Define and lead Azul's AI strategy, including generative AI and predictive models applied to airline operations.
• Govern the portfolio of AI initiatives: prioritization, ROI assessment, ethics of use, and information security.
• Monitor market trends and evaluate the adoption of new models and technologies, with technical and business criteria.

Automation

• Define the strategy and governance model for corporate automation.
• Manage the automation portfolio with a focus on productivity and reduction of rework, using Power Platform, Python, UiPath, and other tools in the ecosystem.
• Establish prioritization criteria, technical standards, and success metrics (e.g., hours saved, error reduction, cycle time) for automation initiatives.

Requirements and qualifications

• Mastery of Snowflake (architecture, cost optimization, data governance).
• Experience with cloud ecosystems (Azure, AWS or GCP).
• Knowledge of relational and non-relational databases (SQL, MongoDB, among others).
• Experience with BI tools and data visualization.
• Experience with automation platforms (UiPath, Power Automate or similar).
• Knowledge of MLOps best practices and the ML/AI model lifecycle.
• Advanced English, for meetings with vendors, reading technical documentation, and presentations to international partners.
• Experience with semantic layers, data contracts, and data catalogs with rich metadata (Snowflake Cortex, dbt Semantic Layer or equivalents); knowledge of LLMOps and frameworks for evaluating models in production (LangSmith, Ragas or equivalents); experience with data observability (data quality monitoring, data SLAs, and proactive alerts).

Behavioral

• Experience leading multidisciplinary and multicultural teams.
• Proven ability to translate business strategy into an executable technical roadmap.
• Transversal influence, with a history of acting in matrix structures and with multiple stakeholders.
• Result-oriented mindset and management by data.
• Executive communication, with experience presenting to C-level and board.
• Adaptability and sense of urgency compatible with the pace of an airline.

Education and Minimum Experience

• Degree in Computer Science, Engineering, Mathematics, Statistics, or related fields.
• Postgraduate degree or MBA in Management, Data, or Technology is a plus.
• 10 to 15 years of experience in technology, with at least 5 years in leadership positions in Data/AI.

Desirable Differentials

• Previous experience in sectors with high transactional volume and real-time data (aviation, retail, fintech, telecom).
• Experience with Claude, GPT or other generative AI platforms in production.
• Experience and certifications in Snowflake, Azure, AWS or GCP.
• Experience leading data architecture transitions — from legacy or BI-oriented environments to modern data products platforms.