• Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations • Build and maintain customer-facing data models and reports in Sigma Computing, embedded within the Skimmer application • Manage and extend ingestion using Fivetran across a growing set of sources • Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse • Partner with product and engineering to turn customer needs into reliable, performant reporting experiences • Collaborate with application engineers to understand source systems and ensure clean, reliable data capture • Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise • Document data models, definitions, and architecture to support a growing team

Role Description As a Senior Data Engineer, you will be at the core of McGraw Hill’s data ecosystem designing, building, and optimizing scalable data solutions that power insights across finance, product, customer, and operational domains. You will work closely with analytics, product, and business stakeholders to translate complex requirements into reliable, high-performance data pipelines and architectures, helping teams make timely and informed decisions. - Design, develop, and maintain scalable data pipelines and solutions using AWS technologies - Build and optimize big data processing workflows using Databricks and Spark - Develop robust ETL/ELT solutions with parallel processing for faster and more efficient data delivery - Design and implement data models (facts, dimensions, star schemas, aggregations) to support reporting and analytics - Monitor and manage cloud-based data platforms to ensure performance, reliability, and SLA adherence - Translate business requirements into technical designs and end-to-end data solutions - Collaborate with cross-functional teams throughout the software development lifecycle, from requirements to deployment - Create and maintain high-quality technical and solution design documentation - Follow Agile/Kanban practices using tools such as Git and Jira Qualifications - A Bachelor’s degree in a related field or equivalent practical experience - 5+ years of experience in Data Engineering or a related discipline - Strong hands-on experience with AWS data services such as Athena, Glue, EMR, Lambda, and Iceberg - Proven experience working with Databricks and Spark-based data processing - Advanced proficiency in SQL and strong Python scripting skills - Solid understanding of modern data warehousing and data lake architectures - Experience with cloud infrastructure and infrastructure-as-code tools such as Terraform - Strong problem-solving skills, attention to detail, and ability to work independently - Excellent communication skills and the ability to collaborate with technical and non-technical stakeholders Requirements - Experience in the Education or Publishing domain (Nice to have) - Familiarity with analytics and BI tools such as Tableau or Microsoft Power BI (Nice to have) - Experience working with financial datasets (sales, revenue, COGS, manufacturing, etc.) (Nice to have) - Exposure to IBM Planning Analytics (TM1) (Nice to have) Benefits There has never been a better time to join McGraw Hill. In our culture of curiosity, collaboration, and innovation, you’ll have the opportunity to work on meaningful problems, own your growth, and help shape the future of learning through data and technology.

• Design large-scale data processing systems. • Work with Product to drive the requirements, and own the project end-to-end. • Analyze and improve the efficiency, scalability, and stability of applications. • Think long-term and be unsatisfied with band-aids. • Identify unnecessary complexity and remove it.

• Implicarse y liderar conversaciones, modelos y decisiones que estructuran los datos de una empresa global, transformando procesos de negocio complejos en modelos de información claros, reutilizables y alineados con la estrategia de datos. • Mantener y evolucionar el Enterprise Data Model y el Data Domain Map de la organización. • Definir conceptos, taxonomías y estándares semánticos que todos puedan entender y aplicar. • Liderar reuniones con stakeholders de negocio para entender cómo funciona el negocio y modelarlo en estructuras de información consistentes. • Colaborar con equipos de datos, arquitectura de soluciones e ingeniería para asegurar la correcta implementación de los modelos en entornos basados en Google Cloud y herramientas de analítica como Power BI. • Definir e impulsar buenas prácticas de modelado en toda la organización, fomentando la adopción de modelos compartidos entre diferentes dominios de negocio. • Garantizar la alineación entre arquitectura de información, gobierno del dato y calidad de datos. • Participar en la mejora continua de herramientas como catálogos de datos y sistemas de gestión de metadatos.