Enterprise Data Architect – Snowflake & Cortex

Position: Enterprise Data Architect – AWS Snowflake & Cortex

Location: Toronto, ON (Hybrid)

Job type: Full time/Permanent hiring

The Enterprise Data Architect is responsible for defining end-to-end architecture for AWS Snowflake-based data, analytics, and AI platforms. Operating at the intersection of data engineering, cloud modernization, and AI enablement, you will design scalable Lakehouse solutions, lead platform migrations, and establish governance, security, and responsible AI standards. This role requires deep expertise in AWS Data Services, Snowflake Cortex, Apache Iceberg, and enterprise data architecture to deliver secure, high-performance, AI-enabled analytics at enterprise scale.

Roles & Responsibilities

• Stakeholder Engagement: Engage with business and technology stakeholders to understand data, analytics, AI, and cloud transformation requirements.
• End-to-End Architecture: Define end-to-end architecture for AWS Snowflake-based data, analytics, and AI platforms.
• Lakehouse Design: Design scalable, secure, high-performance Lakehouse solutions using AWS Data Services, Snowflake, Apache Iceberg / Snowflake Open Catalog, and Snowflake Horizon Catalog.
• Data Engineering & Integration: Architect data ingestion, integration, batch, streaming, and data engineering solutions supporting enterprise analytics and AI use cases.
• AI-Enabled Analytics: Design and implement AI-enabled analytics solutions using Snowflake Cortex, semantic models, and governed data access frameworks.
• Governance & Responsible AI: Establish data governance, security, metadata, lineage, observability, and responsible AI standards across the platform.
• Cloud Transformation: Lead data platform modernization, migration, and cloud transformation initiatives on AWS.
• Architecture Standards: Define architecture standards, reusable frameworks, best practices, and DataOps delivery models.
• Technical Leadership: Provide technical leadership across solution design, implementation, testing, migration, deployment, and production support activities.
• Architecture Reviews: Conduct architecture reviews, identify risks and dependencies, and guide teams on mitigation strategies and best practices.
• Performance & Cost Optimization: Drive platform performance optimization, reliability improvements, and cost optimization across AWS and Snowflake environments.
• Collaboration & Delivery: Collaborate with data engineers, AI specialists, architects, platform teams, and client stakeholders to deliver enterprise-scale solutions.
• Solutioning & Presales: Support solutioning, RFP responses, effort estimation, technical presentations, and client workshops.
• Mentoring & Capability Development: Mentor architects and engineers, contribute to capability development, technical assessments, and knowledge-sharing initiatives.
• Reusable Accelerators: Develop reusable accelerators, reference architectures, implementation frameworks, and best practices for AWS Snowflake and AI solutions.

Required Technical Competencies

• Experience: 14+ years in Enterprise Data Architecture, AWS Data Platforms, Snowflake, Data Engineering, and Cloud Modernization.
• AWS & Snowflake Expertise: Proven experience in Enterprise Solution architecture using AWS, Snowflake, Cortex, and related technology components across the Data-to-Insights end-to-end value chain.
• Hands-On Data Services: Strong hands-on expertise in AWS Data Services (S3, Glue, EMR, Redshift, Lambda), Snowflake, Snowflake Cortex, Snowflake Horizon Catalog, Apache Iceberg / Snowflake Open Catalog, Python, PySpark, and SQL.
• Lakehouse Architecture: Proven experience designing and implementing Lakehouse Architectures, enterprise data platforms, and modern analytics solutions on AWS.
• Snowflake Data Cloud: Strong understanding of Snowflake Data Cloud including Snowflake SQL, Workflows, Snowpipe, Snowflake Model Registry, Snowflake Horizon Catalog, Serverless Compute, and Performance Optimization.
• AI-Enabled Analytics: Experience building AI-enabled analytics solutions using Snowflake Cortex, semantic models, natural language analytics, and governed data access frameworks.
• Data Engineering: Expertise in ETL/ELT, batch and streaming integration, APIs, orchestration, data quality, metadata management, lineage, observability, and DataOps practices.
• Enterprise Data Architecture: Strong knowledge of data models, data products, lakehouse and warehouse patterns, semantic layers, and master/reference data management.
• Security & Compliance: Experience implementing IAM/RBAC, encryption, privacy, masking, auditability, retention, and responsible AI practices.
• Architecture Leadership: Proven experience leading architecture definition, HLD/LLD preparation, NFR assessment, migration planning, technical governance, and production readiness activities.
• Cloud Transformation: Experience supporting cloud transformation initiatives, solutioning, RFP responses, effort estimation, and client-facing architecture discussions.
• BFSI Domain: Good understanding of BFSI regulatory, security, privacy, resilience, and audit requirements.
• Education & Certifications: Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Management, or related discipline; preferred certifications in AWS Solutions Architecture, Snowflake, Data Engineering, Enterprise Architecture, Security, or AI/ML.