Principal Architect & Data Engineer

New

Based in CanadaFull-TimePrincipal

SalaryCompetitive base salary aligned with senior principal engineering roles in Canada (approx. CAD $160,000–$170,000)

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Job Details

10+ years of experience in data engineering, data architecture, or enterprise data platforms.
1+ years in a Staff or Principal-level technical leadership role.
Strong expertise in Python, SQL, and Spark for large-scale data processing and engineering.
Hands-on experience with orchestration tools (e.g., Airflow), CI/CD pipelines, and Infrastructure as Code (Terraform or CloudFormation).
Deep experience with AWS (S3, Glue, Redshift, Bedrock) or Google Cloud (BigQuery, Vertex AI).
Strong knowledge of federated data systems, data mesh principles, and modern data sharing patterns.
Experience building enterprise data governance frameworks, including metadata standards, classification, and PII handling.
Expertise in data security practices, including access control, encryption, masking, and compliance-driven architectures.
Proven experience with RAG architectures, vector databases, and enterprise AI/ML integration patterns.
Strong executive communication skills with the ability to translate complex technical topics into business impact.
Demonstrated leadership in mentoring and developing senior engineering talent.
Strong understanding of cloud economics, cost optimization, and data platform financial governance.
Experience working across large, distributed organizations with multiple stakeholders and priorities.

Design and lead the evolution of a unified enterprise data platform, including Lakehouse architecture (Iceberg/Delta Lake) and federated data mesh models.
Define and implement standards for data governance, observability, quality, lineage, and metadata management across structured and unstructured data.
Develop and scale agentic AI frameworks and enterprise AI registries to support model-agnostic and production-ready AI systems.
Establish best practices for high-performance data engineering, including zero-copy architectures, federated query engines, and semantic layers.
Lead architectural direction across distributed engineering teams, ensuring alignment with enterprise data strategy and governance standards.
Mentor and coach data architects and engineers, fostering technical excellence, automation, and continuous improvement.
Collaborate with business stakeholders to align data products and AI initiatives with KPIs such as revenue growth and operational efficiency.
Present architectural roadmaps, risk assessments, and ROI-driven insights to senior leadership and executive stakeholders.
Define secure data access patterns, including encryption, RBAC/ABAC, masking, and policy-as-code for AI and analytics consumption.
Optimize cloud data platforms for cost efficiency, scalability, and performance across large-scale environments.

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