• Ensure the definition, governance, and evolution of master data in the company's critical domains, acting as an architectural reference and enabling system-wide consistency, operational efficiency, and decision-making based on reliable data. • Define and evolve the company's Master Data Management (MDM) strategy • Map and prioritize critical master data domains • Define entities, attributes, business rules, and official sources (systems of record) • Design architectures for the creation, update, and consumption flows of master data • Specify integration and synchronization standards (event-driven, APIs, batch) • Define and influence data ownership with business areas • Lead or guide MDM implementation initiatives (tools and processes) • Establish and monitor data quality metrics • Work on identifying root causes of issues (not just symptoms) • Prioritize structural fixes versus manual cleansing • Serve as a reference for squads and architectures in adopting the master data model • Act as a bridge between business, data, and technology

• Design, develop, and implement robust pipelines that connect data from diverse sources using primarily tools from the AWS ecosystem • Implement scalable and efficient data enrichment solutions using DBT to transform raw data into tailored data models and actionable business insights • Manage integrations, transformation processes, and task automation using data connectors, SQL APIs, Python, and REST • Use SQL to query and manipulate data in relational and non-relational databases • Apply Spark for processing large volumes of data in real time and in batch

• Design, develop, and implement robust data pipelines, integrating data from diverse sources primarily using tools from the AWS ecosystem; • Understand business and software product requirements and translate them into efficient, scalable data products; • Serve as a technical reference for the architectural design of data-driven and AI-first solutions, coordinating across product, analytics, and ML to define standards, best practices, SLAs, observability, and platform governance; • Implement scalable, efficient data enrichment solutions using DBT to transform raw data into tailored data models and actionable business insights; • Manage integrations, transformation processes, and task automation using data connectors, SQL APIs, Python, and REST; • Use SQL for querying and manipulating data in relational and non-relational databases; • Apply Spark for processing large volumes of data in real-time (Kafka and similar) and batch.

• Build infrastructure that enables analytics and data science teams to deliver innovative, impactful solutions for clients. • Assist clients in migrating their existing business intelligence and data warehouse solutions to Google Cloud. • Design, develop, and optimize robust data pipelines, making data easily accessible for visualization and machine learning applications. • Design and implement new data warehouse and data mart solutions including transforming, testing, deploying, and documenting data. • Architect, maintain, and troubleshoot cloud-based infrastructure to ensure high availability and performance. • Work closely with technology partners such as Google Cloud, Snowflake, dbt, and Looker, mastering their technologies and building a network with their engineers. • Collaborate in an agile and dynamic environment with a team of data engineers, BI analysts, data scientists, and machine learning experts. • Implement software engineering best practices to analytics processes, such as version control, testing, and continuous integration.