Sr Data Engineer

Build the Future

At McGraw Hill, we are dedicated to delivering digital learning experiences that transform education for learners and educators. Our focus is on creating seamless, impactful products that truly benefit our users while supporting growth and collaboration across teams. We foster a culture that values innovation, teamwork, and a balance between career growth and personal well-being.

The Senior Data Engineer will play a key role in advancing McGraw Hill Education's (MHE) product eventing, data platforms, and analytics capabilities. This role focuses on designing scalable, reliable, and high-quality data solutions that transform learning interactions and product events into actionable learning insights.

Embedded within product platform teams, the engineer will collaborate closely with product managers, software engineers, and data teams to define and create meaningful events, build end-to-end event pipelines, and ensure data quality from learning interaction to learning insight.

The ideal candidate brings strong hands-on expertise in AWS, Databricks, and modern event-driven data architectures, with experience designing real-time or near-real-time event pipelines, Delta Lake, and medallion (Bronze/Silver/Gold) data architecture patterns. Proficiency in SQL and Python or Scala for large-scale data transformation is essential. Experience with Databricks Workflows, infrastructure-as-code, and CI/CD practices is a plus.

This role requires a strong focus on product data, event standardization, pipeline reliability, and end-to-end data quality. The engineer will help establish robust data foundations that enable trusted insights across MHE's learning products and platforms, supporting the organization's ability to turn learner interactions into meaningful learning outcomes.

• Work closely with product platform teams to define and create meaningful learning events, establish event standards, and build scalable, reliable event pipelines that connect learning interactions to actionable learning insights.
• Demonstrate hands-on experience designing and delivering data solutions on Databricks, including Delta Lake, medallion (Bronze/Silver/Gold) architecture, Unity Catalog, and scalable lakehouse design patterns on AWS.
• Design, develop, and optimize parallel and distributed ETL/ELT pipelines using Apache Spark (PySpark/Scala) and Databricks. Implement efficient data transformations, joins, aggregations, window functions, and other Spark-native capabilities to support high-volume product and learning event data.
• Build and maintain real-time and near-real-time event-driven data pipelines that support the ingestion, processing, and transformation of product and learning interactions. Ensure reliable data flow from event creation through curated data layers and learning insights.
• Establish and maintain end-to-end data quality across event schemas, ingestion pipelines, transformations, and curated data products. Implement validation, monitoring, and alerting to ensure data accuracy, consistency, completeness, and timely delivery.
• Develop and maintain Databricks Workflows, including dependency management, retry policies, scheduling, monitoring, and alerting. Build maintainable, cloud-native pipeline orchestration solutions.
• Apply partitioning, caching, broadcast joins, and other performance optimization techniques to improve pipeline efficiency, scalability, and throughput. Use Delta Lake capabilities to support reliable and maintainable data processing.
• Design and implement scalable data models using Delta tables, including fact tables, dimension tables, star and snowflake schemas, and aggregations where appropriate. Ensure curated data assets support product analytics and learning insights.
• Apply modern data architecture and governance principles using Unity Catalog, Delta Sharing, and AWS-native lakehouse patterns to support secure, discoverable, and trusted data products.
• Use Git-based version control, including Databricks Repos/Git folders, and project management tools such as Jira within Agile/Kanban delivery frameworks.
• Translate product and data requirements into technical designs and production-grade solutions, contributing across the full software development lifecycle-from requirements gathering and architecture through implementation, testing, deployment, and ongoing support.
• Develop…