Interactive software transforming higher education engagement
Top Hat is a Toronto-based education technology company that provides interactive software solutions for higher education institutions. With over 500,000 students engaged across North America, Top Hat empowers educators to create and distribute their own multimedia online textbooks, significantly re...
Employees enjoy competitive health benefits, lifestyle spending accounts, and access to the latest technology. Top Hat also offers a flexible remote w...
Top Hat fosters a culture of innovation by digitizing educational content, making it more accessible and engaging for students. The company values col...
Top Hat is transforming higher education by making learning more personal, engaging, and effective. We bring together interactive content, assessment, and analytics to spark better teaching and learning for a brighter world. As we continue to build our AI-first, personalized learning experiences powered by cutting-edge data science, our Data team plays a pivotal role in shaping that vision.
We are seeking a Data Engineer to join our Data Platform team at Top Hat. In this role, you’ll be at the heart of shaping how data is organized, trusted, and delivered across the company. The work you do will directly impact the reliability and accessibility of the data that powers product innovation, personalization, and decision-making at scale.
Playing a central role in building robust BI (dimensional) and ER models that power analytics, reporting, and product-facing features. Contribute to medallion-style architecture as a layered approach to data delivery, but prioritize fit-for-purpose dimensional and ER models where they drive the most value.
Applying strong data modelling practices to deliver clear, performant, and future-proof schemas that drive both operational and analytical workloads.
Helping establish standards and practices that allow our teams to operate with confidence, whether they’re building new AI features or analyzing business performance.
Tackling challenges in data modelling, governance, and quality to ensure our data is not only available but trusted by everyone who depends on it.
Helping establish standards and practices that allow our teams to operate with confidence, whether they’re building new AI features or analyzing business performance.
Being a part of a team that is moving fast on modernization and scalability, taking legacy data systems and transforming them into a robust, future-ready platform.
Design & Model Data: Build and optimize BI-oriented dimensional models (star/snowflake) and ER data models that support business-critical analytics and product use cases. Support data models in a layered (medallion-style) architecture to support business-critical and product-facing use cases.
Build Pipelines: Develop and maintain reliable, scalable ETL/ELT pipelines using SQL, Python/Scala, and orchestration tools (e.g., Airflow, MWAA).
Ensure Data Quality & Governance: Implement validation frameworks, manage access controls, and handle PII data responsibly to build trust in the platform.
Work with Complex Data: Transform and optimize structured and semi-structured data (JSON, Avro, Parquet) and address schema evolution challenges.
Expand Capabilities with Graph: Apply graph database concepts (e.g., Neo4j) for lineage, metadata, or relationship-driven use cases.
Collaborate Cross-Functionally: Partner with analytics, product, and data science teams to translate requirements into robust and accessible datasets.
Data Engineering Experience: 3+ years building production-grade pipelines and data assets.
Data Modelling: Solid understanding (Intermediate) in layered/medallion architectures and entity modelling.
SQL: Strong proficiency in query tuning and optimization
ETL/ELT Development: 3-4 years using Python (or Scala) for production-grade transformations.
Cloud Data Platforms: Hands-on with at least one or multiple cloud platforms (AWS, GCP, or Azure).
Lakehouse/Warehouse Tech: Practical experience with Athena, Redshift, BigQuery, Snowflake, or Databricks.
Pipeline Orchestration: 3+ years using orchestration frameworks (Airflow, MWAA, Dagster, etc.) and familiarity with CI/CD pipelines for deployment.
Structured & Semi-Structured Data: Working familiarity and optimization
Data Quality & Governance: Proven experience implementing governance, access controls, and PII handling (Senior).
Graph Databases: 1–2 years experience with graph modelling and query optimization
Event-Driven Architectures: Expected 2–3 years for Senior (Kafka, Kinesis, Pub/Sub).
Communication & Collaboration: Strong ability to work cross-functionally; senior engineers also mentor and influence decisions.
Experience with event-driven ingestion at scale.
Familiarity with data catalog or metadata management tools
Exposure to customer-facing data products or APIs.
Why team members love working at Top Hat:
A noble mission that creates meaningful, fulfilling work
A team that cares deeply for customers and for each other
Flexible, remote first work environment
Professional learning and development for all role levels
An awesome and welcoming Toronto HQ
Competitive health benefits that start on day one
A management team focused on performance, growth, engagement and connection
Our winning strategy and market potential
Innovative PTO policy with lots of time and space for self-care
Passionate customers that believe in us—and what we do
A chance to work with new tech like generative AI—and see the customer impact
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