Principal Data Engineer

This organization operates large-scale broadband infrastructure across Canada, spanning fibre, wireless, and satellite networks. Its data environment brings together network and operational systems, CRM data, and other business platforms.

The Role

This is a senior technical leadership role focused on building the company’s enterprise data platform and raising the engineering bar across the team. You will own architecture across ingestion, storage, modeling, governance, quality, and observability, while helping consolidate fragmented on-prem and cloud data into a scalable lakehouse environment.

What You Will Do

• Architect and deliver an enterprise lakehouse using bronze, silver, and gold layers for analytics, data science, and AI workloads.
• Build reliable batch and streaming pipelines across on-prem databases, network telemetry, CRM platforms, APIs, and cloud systems.
• Establish data governance, lineage, cataloging, access controls, quality standards, and observability across the platform.
• Set engineering standards around CI/CD, infrastructure as code, testing, performance tuning, and cloud cost management.
• Create reusable architecture patterns while mentoring data engineers and working with technical and business teams to turn requirements into scalable data products.

What You Bring

• 10+ years of data engineering experience, including at least 4 years leading platform or team-level initiatives.
• Deep hands-on experience with lakehouse architecture, distributed compute, cloud storage, and batch and streaming data systems.
• Strong Python and/or Scala skills, advanced SQL, and experience tuning large-scale ELT workloads.
• Experience integrating Salesforce, on-prem relational databases, APIs, files, and telemetry into cloud data environments.
• Strong knowledge of enterprise governance, lineage, security, privacy, CI/CD, infrastructure as code, automated testing, and data observability, including familiarity with Canadian privacy requirements.

Why This Role

You will have ownership across both the technical direction of the data platform and the engineering practices behind it. The scope covers architecture, governance, reliability, cost, and team development, with the platform supporting BI, data science, and AI use cases across the business.