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: Strong 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 Qualified
- Cloud Architect Certifications (e.g., AWS Certified Solutions Architect, Google Professional Cloud Architect, or Azure Solutions Architect Expert)

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