• Design, build, and optimize Databricks pipelines in an Azure environment • Develop reliable ETL/ELT workflows using PySpark / Spark SQL • Implement and maintain data ingestion from multiple sources (APIs, DBs, cloud storage, etc.) • Improve performance, scalability, and cost-efficiency of Databricks workloads • Work with stakeholders to translate business needs into technical solutions • Ensure best practices around data quality, monitoring, documentation, and maintainability • Collaborate with cross-functional teams (data engineering, analytics, product, etc.)

• Ability to design data pipelines with scalability, high performance and resilience in-built • Ability to code complex requirements as part of large complex data platforms - develop, refine, and implement high quality delivery executions • Deliver project deployments on public clouds and highly secure environments • Integrate with data governance tools, ecosystem products across the cloud landscape • Ability to understand the business requirements and translate into high-level and low-level designs to successfully meets the business objectives. • Collaborate with stakeholders, presenting findings to a non-technical audience • Bring in best practices for coding and building modular data engineering code that can be reused • Stay current with technical and industry developments and standards to ensure effective and advanced applications of data analysis techniques and methodologies. Including AI integrations.

• 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.