• As part of our Data Engineering team, you will work on customer-specific solutions: you are responsible for developing interfaces to existing data sources (SQL, files, APIs, etc.) • Developing data preparation, aggregation, and cleansing pipelines (ETL) is also part of your responsibilities • Documenting data structures in a data dictionary is standard practice for you • As well as storing data in a central data store (SQL, NoSQL, etc.) • You develop RESTful API services using established frameworks and standards (Flask, Swagger) • You write automated tests and documentation, as well as implement CI/CD pipelines • You ensure deployment across various cloud environments, often based on container technologies (Docker, Kubernetes, OpenShift) or native cloud services (e.g., serverless)

• Design and evolve the semantic modeling layer that serves as the single source of truth for metrics, dimensions, entities, and business logic across all data products • Define the standards for how semantic models are authored, versioned, tested, and governed • Evaluate and drive the semantic layer technology strategy • Architect how the semantic layer is consumed across three distinct product surfaces • Ensure the semantic layer is highly performant and scalable as data volumes and consumer demand grow • Build the semantic layer as a true platform experience • Operate as a technical leader across the Data & Reporting Platform organization • Drive adoption patterns and build team-specific contexts and workflows using AI coding tools

• Design, build and maintain robust, scalable data infrastructures on AWS. • Implement and manage big data solutions using Apache Spark. • Develop and optimize data pipelines, ETLs and complex data workflows. • Write complex SQL queries for data analysis and insight generation. • Use Databricks as a central platform for data analytics. • Ensure data quality and compliance with data security and privacy standards. • Collaborate with cross-functional teams to understand business needs.

• Serás responsable de diseñar, construir y mantener arquitecturas de datos que permitan procesar grandes volúmenes de información de forma eficiente, segura y escalable. • Diseñar arquitecturas en la nube utilizando herramientas de GCP como BigQuery y Pub/Sub. • Implementar y mantener pipelines de datos optimizados que soporten grandes volúmenes de información en tiempo real. • Desarrollar y gestionar bases de datos SQL asegurando un rendimiento óptimo. • Implementar procesos de ETL (Extract, Transform, Load) para integrar datos de múltiples fuentes.