• 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

• 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.

• Design, implement, and maintain data ingestion, processing, and storage solutions using Azure platform tools such as Azure Databricks, Azure Data Factory, Azure Data Lake, and SQL databases; • Develop and optimize ETL (Extract, Transform, Load) workflows using PySpark to ensure data integrity and quality; • Model and structure data efficiently to meet the organization's analytics and BI requirements; • Monitor and troubleshoot data infrastructure issues, ensuring system availability and scalability.

• Design and implement scalable, high-throughput data ingestion systems that integrate internal and external data across domains • Design and build core data platform components, including ingestion, validation, orchestration, and lineage, with a focus on code quality and reliability • Build and evolve a centralized data lake using Apache Iceberg (or similar table formats) • Work across multi-cloud environments (AWS, GCP, Azure) to design and implement cloud-agnostic data ingestion and processing patterns • Contribute hands-on to the Semantic layer, ensuring data is easy to consume for BI and analytics teams • Partner with Senior Data Engineers, Platform Engineers, and Analytics Engineers to align how data is produced, stored, and consumed • Establish practical engineering standards for testing, observability, and operational excellence • Provide technical leadership through mentorship, code reviews, and design discussions, while remaining hands-on