• Design, build, and maintain reliable end-to-end ETL pipelines orchestrated with Apache Airflow • Integrate data from multiple sources (internal operational databases, third-party APIs, SaaS tools) into the Google Cloud Data Warehouse (BigQuery) • Design and evolve data models, warehouse schemas, and transformations to support scalable analytics and KPIs • Ensure data quality, reliability, and observability through monitoring, validation, and alerting • Own the product data structure, mapping product features and behaviors to analytics-ready data models • Define and maintain meaningful KPIs in collaboration with Product and BI • Enable analytics for AI-powered product features, ensuring visibility on usage, performance, quality, and business impact • Partner with Product, BI, and other stakeholders to gather requirements and deliver dashboards and reports • Maintain clear and up-to-date documentation for data models, pipelines, and metrics • Act as the primary bridge between Backend Engineering and BI, owning the flow from data production to analytics consumption • Triage, analyze, and address BI requests related to data availability, correctness, performance, and modeling • Collaborate with Backend Engineers on data contracts, schema evolution, and performance optimization, without owning core backend services • Proactively identify and resolve data-related issues impacting BI and Product teams • Own first-level monitoring and support for data pipelines and Airflow DAGs, ensuring timely resolution of failures • Collaborate with BI and Backend teams to troubleshoot and resolve complex issues • Continuously improve the stability, performance, and maintainability of the data platform

• 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.