Data Engineer, Data Platform

QuadReal Property Group•Toronto, ON

1d•CA$85,000 - CA$100,000•Onsite

About The Position

Reporting to the Team Lead, Data Platform, this role will contribute to building, scaling, and optimizing our Enterprise Data Platform on Microsoft Fabric and Azure. This is a hands-on engineering role for someone who thrives in code, designing and delivering high-quality data pipelines, APIs, and integrations using modern Python-based tools. We are especially interested in engineers who come from a software development background and have since moved into the data space, bringing strong software engineering discipline (clean code, testing, version control, and automation) to how data pipelines are built and operated. You will collaborate with Data Governance, IT, and business teams to ensure our solutions are scalable, maintainable, secure, and trusted. Alongside development, you will help apply and improve platform standards, share what you learn with the team, and work closely with stakeholders to deliver reliable, production-ready solutions.

Requirements

Bachelor’s degree in computer science, Engineering, Information Systems, Business Technology Management, or a related field.
2 to 4 years of experience in the data field (data engineering or similar roles), ideally with a background in software development that has since transitioned into data.
Strong Python skills: comfortable writing clean, efficient, production-grade code for pipelines, APIs, and data transformations. (Required)
Solid proficiency in SQL for complex querying and data manipulation. (Required)
Working knowledge of Microsoft Fabric (Lakehouses, Warehouses, and workspaces) and Microsoft Azure data services.
Experience with workflow orchestration using Apache Airflow.
Experience building data processing workloads with PySpark.
Familiarity with containerizing workloads using Docker.
Exposure to building data transformations with dbt.
Familiarity with modern data architectures (data lakehouse, data mesh, warehousing best practices).
Strong communication and collaboration skills, with a willingness to learn from and support teammates.

Nice To Haves

Experience integrating pipelines with governance tools (Microsoft Purview) is preferred.
Knowledge of CI/CD workflows using Azure DevOps and Git is preferred.
Experience using Infrastructure as Code (IaC) with Terraform / Terragrunt to automate provisioning of cloud data environments and platform components, ideally in Azure, is an asset.

Responsibilities

Develop, test, and deploy robust, maintainable data pipelines and APIs using Python, dbt, Apache Airflow, and PySpark.
Build and maintain Microsoft Fabric Lakehouses, Warehouses, and workspaces within the enterprise data platform.
Build and optimize large-scale, distributed data processing workflows on Microsoft Fabric and Azure.
Package and deploy data workloads using Docker, following the team’s reproducible release practices.
Implement data quality, validation, observability, and lineage controls to meet enterprise governance and compliance requirements.
Contribute to the technical design of platform components and take part in the architecture discussions that shape how the team builds.
Apply and help strengthen the team’s engineering standards: testing patterns, reusable frameworks, and code review practices, with clean, readable Python as the baseline.
Build governance and security into what you deliver, using Microsoft Purview for classification, tagging, and access control.
Work closely with Data Governance, Data Solutions, Advanced Analytics, and IT Security teams to ensure business alignment.
Take an active part in code reviews and pairing, both giving and acting on feedback to raise the team's overall bar.
Contribute to a culture of continuous learning, experimentation, and knowledge sharing.