Principal Data Architect — Databricks Enablement

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Data Architect — Databricks Enablement based in Canada. This is a senior architecture role focused on designing sophisticated cloud-based data and analytics solutions for enterprise customers. You will provide strategic technical leadership across data engineering engagements, helping organizations turn complex data challenges into scalable solutions. The role combines architecture, consulting, technical delivery, pre-sales support, and engineering leadership. You will work across traditional data platforms, big data, machine learning, MLOps, generative AI, and modern DataOps practices. You’ll also help establish standards, operating practices, and reusable approaches that strengthen the broader data engineering organization. The environment is fully remote and globally distributed, with significant autonomy and opportunities to influence both customers and internal technical teams. This position is ideal for an experienced data architect who enjoys solving complex problems and guiding teams toward practical, modern cloud solutions. Define strategic data and technology roadmaps for customers, translating business objectives into targeted technical outcomes and actionable plans. Serve as a senior data engineering subject matter expert during the definition and planning of upcoming customer engagements. Partner with pre-sales teams to assess customer requirements, shape solution approaches, and contribute technical expertise to proposed engagements. Lead the architecture and design of complex cloud-based data and analytics solutions across diverse customer environments. Establish and oversee data standards, operating procedures, documentation, and repeatable implementation practices. Provide technical guidance and mentorship to Architects and Engineers, helping teams make sound architectural and engineering decisions. Conduct technical interviews for architecture and engineering candidates and contribute to maintaining a strong technical talent bar. Promote consistent, scalable, and well-documented approaches to cloud data engineering across projects and teams. Support the adoption of modern practices spanning data engineering, analytics, machine learning, generative AI, and DataOps. Collaborate with customers and internal stakeholders to identify opportunities for modernization, optimization, and improved data capabilities. Requirements: 10+ years of experience designing and building complex data systems, including substantial experience working within cloud data environments. At least 4 years of experience working across the AWS data landscape. Strong expertise across multiple areas of traditional data, including relational database design, optimization and migration; transactional and analytical data modeling; BI dashboards and visualization; data governance; and master data management. Strong expertise across multiple areas of modern data, including Spark and big data processing, streaming, NoSQL, machine learning and MLOps, generative AI, foundational models, and DataOps. Experience with machine learning and MLOps using platforms such as SageMaker is an asset. Experience with generative AI foundational models and associated frameworks, including platforms such as Bedrock. Strong understanding of DataOps practices, including infrastructure as code, data testing, data versioning, and automated delivery. Experience with at least two infrastructure-as-code tools, CI/CD platforms or tools, and/or Python for analytics and automation. Strong drive to standardize, document, and continuously improve technical approaches and solution architectures. Excellent written and verbal communication skills, with the ability to explain complex technical concepts to both technical and business audiences. Strong consulting mindset and ability to operate effectively with customers, pre-sales teams, engineers,