We are seeking a visionary and highly technical Snowflake Enterprise Data Architect to lead the strategy, design, and implementation of our next-generation enterprise data platform. In this role, you will be the principal architect responsible for transforming our data landscape into a scalable, secure, and high-performing ecosystem centered around Snowflake. You will bridge the gap between business strategy and technical execution, ensuring our data architecture supports advanced analytics, AI/ML initiatives, and seamless data democratization across the enterprise. Key Responsibilities Architecture & Strategy Design & Strategy: Define the enterprise data architecture blueprint, roadmaps, and patterns using the Snowflake Data Cloud. Data Modeling: Architect scalable conceptual, logical, and physical data models (Data Vault 2.0, Star/Snowflake Schema, 3NF) optimized for Snowflake's micro-partitioning and storage architecture. Migration Leadership: Lead the architectural strategy for migrating legacy data warehouses (e.G., Teradata, Oracle, Netezza, or on-prem Hadoop) to Snowflake. Implementation & Optimization Performance Tuning: Optimize Snowflake performance through strategic warehouse sizing, clustering keys, materialized views, search optimization services, and query tuning. Cost Management: Implement robust cost-governance frameworks, resource monitors, and consumption tracking to ensure highly efficient utilization of Snowflake credits. Data Integration (ETL/ELT): Design streaming and batch data ingestion pipelines using Snowpipe, Streams, Tasks, Kafka, and modern ELT tools (e.G., dbt, Matillion, Fivedran, or Airflow). Governance, Security & Collaboration Data Security & Compliance: Enforce rigorous data security policies within Snowflake, including Role-Based Access Control (RBAC), Row-Level Security (RLS), Column-Level Security (CLS), data masking,



and end-to-end encryption. Data Sharing: Drive data monetization and collaboration strategies utilizing Snowflake Marketplace, private data exchanges, and clean rooms. Cross-Functional Collaboration: Partner with Data Engineers, Data Scientists, BI teams, and business stakeholders to translate complex business requirements into robust technical solutions. Technical Skills & Competencies Core Snowflake Mastery: Deep expertise in advanced Snowflake features (e.G., Snowpark, Iceberg tables, Dynamic Tables, Time Travel, Fail-Safe, Zero-Copy Cloning, and UDFs/Stored Procedures using Python/SQL). Cloud Ecosystems: Solid experience architecting cloud data solutions on at least one major cloud provider (AWS, Azure, or GCP) and integrating native services with Snowflake. Data Engineering & Code: Proficiency in SQL and Python or Scala. Strong grasp of software engineering best practices applied to data (CI/CD pipelines, Git, Terraform/DataOps). Data Governance: Familiarity with modern data governance and cataloging tools (e.G., Collibra, Alation, Apache Atlas). Qualifications & Experience Experience: 10+ years of experience in data engineering, data warehousing, and enterprise data architecture, with at least 4+ years of dedicated experience designing production-grade architectures on Snowflake. Proven Track Record: Successfully led at least two large-scale enterprise data migrations from legacy systems to Snowflake. Education: Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related technical field (or equivalent professional experience). Preferred Certifications Snowflake Certified Advanced: Data Architect (Highly Preferred) Snowflake Certified Core Professional Cloud Architect Certifications (e.G., AWS Certified Solutions Architect, Google Skilled Cloud Architect, or Azure Solutions Architect Expert) #J-18808-Ljbffr

📌 Snowflake Enterprise Data Architect (Markham)
🏢 KData AI
📍 Markham