Data Engineer

Top Hat • Full-time • Remote (Canada) • 1m ago

Fast Facts

Top Hat is seeking a Data Engineer to join its Data Platform team, where you will play a crucial role in shaping data organization and delivery to support analytics and product innovation in higher education.

Responsibilities: Design and model data for business-critical analytics, build and maintain ETL pipelines, implement data quality and governance measures, work with complex data structures, and collaborate with cross-functional teams.

Skills: Proficiency in SQL, Python/Scala, ETL/ELT development, experience with cloud data platforms, and knowledge of data quality practices; familiarity with graph databases and event-driven architectures preferred.

Qualifications: 3+ years of data engineering experience with a solid understanding of data modeling, pipeline orchestration, and cloud platforms.

Location: This position is located in Canada.

Compensation: Not provided by employer. Typical compensation ranges for this position are between CAD 80,000 - CAD 120,000.

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.

The Opportunity

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.

You will be instrumental in:

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.

You will:

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.

You are:

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.