Vitta Corporation

• Act in a consultative capacity alongside business teams, understanding pain points and proposing data-driven solutions; • Lead end-to-end data science projects (from problem definition to production delivery); • Translate business problems into analytical approaches (predictive models, segmentations, optimizations, etc.); • Communicate results and insights clearly to technical and non-technical stakeholders; • Ensure the quality, reproducibility, and governance of developed models; • Communicate technical points, decisions, and trade-offs to peers and stakeholders;

• People Management: Lead, mentor, and develop the technical team, fostering career plans and providing regular feedback (1:1s). • Strategic Planning: Define the area roadmap aligned with business objectives and ensure delivery of critical projects. • Vendor Management: Serve as the primary interface with technology partners and service providers. • FinOps: Manage the cloud budget, identifying cost-saving opportunities (Reserved Instances, Savings Plans, etc.) and preventing waste. • AWS Operations and Support: Ensure the resilience, scalability, and availability of the cloud environment. • Offensive and Defensive Security: Oversee the operation of tools such as SIEM, WAF, firewalls, and Prisma. • DevOps Culture: Support the team in implementing and improving CI/CD pipelines and ensure all infrastructure is provisioned via Terraform (IaC). • Incident Response: Act as the focal point during security incidents or outages, leading root cause analysis.

• Data Collection: Collect data from various internal and external sources, following established procedures and proposing process improvements when necessary. • Data Preparation: Perform data cleaning, transformation, and integration tasks to ensure data quality and consistency with minimal supervision. • Project Support: Collaborate with team members on the development and maintenance of data pipelines, taking on larger responsibilities and leading small subprojects. • Process Documentation: Make significant contributions to data engineering process documentation by recording procedures and workflows and ensuring they remain up to date. • Exploratory Data Analysis: Conduct initial data analyses to identify patterns, trends, and business-relevant insights with greater autonomy. • Query Support: Execute more complex SQL queries and extract information from relational databases, providing technical support to the team. • Quality Monitoring: Proactively monitor data quality, identify and resolve issues or inconsistencies, and suggest improvements to quality control practices. • Continuous Learning: Participate in and contribute to training and development programs, and mentor junior team members. • Cross-Functional Collaboration: Work effectively with other departments to ensure proper data integration into solutions and systems, contributing specialized knowledge. • Meeting Deadlines: Manage tasks and ensure completion within established deadlines, proactively communicating any challenges or obstacles to team leadership.