• Lead end-to-end Data Science projects using agile methodologies with a focus on delivering business value. • Perform exploratory analyses to identify patterns, trends and generate actionable insights to support decision-making. • Develop, evaluate and implement statistical, predictive and prescriptive models (supervised and unsupervised). • Act in a consultative manner to understand client pain points and needs throughout the project. • Present results, analyses and recommendations to technical and non-technical stakeholders with clarity and storytelling. • Collaborate actively with the team, sharing knowledge, best practices and new technical approaches. • Support internal requests and strategic initiatives when needed.

• Design, develop, and maintain robust, scalable data architectures in cloud or hybrid environments, aligned with clients' business needs. • Build and optimize data pipelines (batch and streaming), ensuring reliable ingestion, processing, and delivery of information. • Work with large volumes of data from multiple sources, focusing on solving business problems. • Model data for analytical, predictive, and prescriptive consumption, supporting data science teams and business stakeholders. • Ensure data security, governance, and quality, complying with internal policies and regulatory requirements such as LGPD (Brazilian Data Protection Law). • Identify opportunities for continuous improvement in data flows, promoting efficiency, cost reduction, and increased reliability. • Serve as a technical reference for the team and the client, supporting architectural decisions, providing technical mentorship, and promoting best practices.

• Set the technical direction for the data engineering team • Own the strategy, maintenance, and operations of our data platform • Lead a team of data engineers while staying hands-on with architecture decisions and technical leadership • Maintain and build feature stores and ML infrastructure to power our machine learning models

• Provide guidance and mentorship to managers and individual contributors on the team. • Develop strong relationships with data science, engineering, and business partners, and improve how we leverage data at Netflix. • Help nurture the data engineering community at Netflix, which extends beyond your immediate team and requires working with other senior leaders • Expand your team by attracting diverse talent that can push our technical boundaries. • Foster an inclusive environment, where all points of view are welcomed and encouraged. • Be a thoughtful leader, and drive both technical and business priorities and roadmaps.