Role: Azure Senior Data Lead SHORTDESCRIPTION section. 1 of 6. Section Title: Job Summary Job Summary Role – Sr. MLOps Technical LeadLocation – Anywhere in CanadaSell rate – CAD 130 to CAD 150 per hourMLOps Technical Lead to drive the design, implementation, governance, and operationalization of enterprise AI/ML platforms built on Microsoft Azure and Databricks. The ideal candidate combines deep technical expertise in MLOps and cloud architecture with strong business analysis and stakeholder management capabilities. LONGDESCRIPTION section. 2 of 6. Section Title: Key Responsibilities Key Responsibilities MLOps & Platform LeadershipDefine and implement enterprise MLOps strategy, standards, and best practices.Lead the design and deployment of end-to-end ML lifecycle solutions including:Data ingestion and preparationFeature engineeringModel training and experimentationModel deploymentMonitoring and retrainingEstablish CI/CD and CT (Continuous Training) pipelines for ML workloads.Drive model governance, reproducibility, versioning, lineage, and compliance.Implement observability frameworks for ML model performance, drift detection, and operational monitoring.Lead technical reviews and architecture governance for AI/ML initiatives. LONGDESCRIPTION section. 3 of 6. Section Title: Skill Requirements Skill Requirements Azure & Databricks ArchitectureDesign scalable and secure cloud-native AI platforms leveraging:Azure DatabricksAzure Machine LearningAzure Data FactoryAzure Data Lake Storage Gen2Azure DevOps / GitHub ActionsAzure Key VaultAzure Monitor / Log AnalyticsAzure Kubernetes Service (AKS)Microsoft FabricDefine reference architectures for batch, streaming, and real-time ML workloads.Ensure alignment with enterprise security, networking, and compliance standards.Optimize platform cost, performance, and reliability.Solution ArchitectureTranslate business requirements into scalable AI/ML solution architectures.Develop architecture blueprints, solution designs, and technical roadmaps.Evaluate emerging technologies and recommend platform enhancements.Lead architecture workshops and design thinking sessions.Define integration patterns with enterprise systems and data platforms.Business Analysis & Stakeholder ManagementEngage business stakeholders to understand strategic objectives and use cases.Conduct requirements gathering, gap analysis, and feasibility assessments.Define business outcomes, KPIs, and success metrics for AI initiatives.Create business cases and ROI assessments for AI/ML investments.Collaborate with Product Owners and business teams to prioritize AI capabilities.Facilitate communication between business and technical teams.Team LeadershipLead multidisciplinary teams comprising Data Engineers, Data Scientists, ML Engineers, and Cloud Engineers.Provide technical mentoring and coaching.Drive Agile delivery and DevOps practices.Establish engineering standards and operational excellence practices. SKILL section. 4 of 6. Section Title: Must Have Skills Must Have Skills Click Enter to show the proficiency description of PythonPython Click Enter to show the proficiency description of Azure DatabricksAzure Databricks Click Enter to show the proficiency description of Azure Data Factory (ADF)Azure Data Factory (ADF) Click Enter to show the proficiency description of MS SQLMS SQL Click Enter to show the proficiency description of Oracle PL/SQLOracle PL/SQL SKILL section. 5 of 6. Section Title: Good to have Skills Good to have Skills LONGDESCRIPTION section. 6 of 6. Section Title: Other Requirements Other Requirements 1.Relevant certifications in Azure Data Factory, Azure Databricks, SQL, Oracle, or Python would be a plus.