Job Summary: Serve as a technical and strategic reference in data architecture, construction, and governance, promoting innovation and efficiency in large-scale data solutions. Key Highlights: 1. Technical and strategic leadership in data architecture 2. Promotion of innovation and efficiency in data solutions 3. Driving a data-driven culture and reducing data silos Job Mission Serve as a technical and strategic reference in data architecture, construction, and governance, promoting innovation and efficiency in large-scale data solutions. Academic Qualifications * Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or related fields (currently pursuing or completed). * Preferred: MBA, master’s degree, or advanced certifications in Data Engineering, Big Data, or Solution Architecture. Years of Experience * 6–8 years of experience working with data in highly complex environments. Behavioral Competencies * Technical leadership with a mentoring and collaborative profile. * Strategic thinking focused on innovation. * Ability to handle ambiguity and prioritize demands. Technical Competencies * Expertise in data architectures (Data Lake, Lakehouse, Data Mesh, etc.). * In-depth knowledge of data processing languages (Python, Spark). * Proficiency in cloud environments (GCP, AWS, Azure) and Big Data solutions. * Experience with data governance and data catalog tools. * Practical experience with compliance and security (LGPD, GDPR, etc.). Day-to-Day Activities * Define and implement scalable and secure data architectures. * Lead strategic data initiatives across multiple business areas. * Mentor Level I and Level II Data Engineers. * Participate in defining the corporate data strategy. Value-Generating Activities * Drive a data-driven culture across the organization. * Reduce data silos and expand strategic use of information. * Ensure compliance and scalability in data solutions. Responsibilities * Be the guardian of best practices in data engineering. * Lead technical decisions in strategic projects. * Collaborate with data scientists, analysts, and enterprise architects. Challenges * Scale solutions in dynamic environments with multiple data sources. * Lead the transformation of data into a strategic asset. * Balance innovation and efficiency with security and governance.