Sr. Technical Architect
Snowflake
| Company | Snowflake |
| Category | Engineering |
| Location | Toronto |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Senior |
| Salary | USD 180k |
| Posted | 13 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
We are hiring a Senior Technical Architect for our Services Delivery team. Our SD organization partners with customers across every stage of their data and AI journey, from initial implementation through long-term platform strategy. In this role, you will own the technical delivery and outcome for complex, high-priority engagements, serve as the most senior technical voice in the room, and shape how our customers use Snowflake to drive real business transformation.
AS A SENIOR TECHNICAL ARCHITECT AT SNOWFLAKE, YOU WILL:
Customer Leadership and Advisory
- Lead customer engagements as the primary technical authority, owning architecture decisions and driving measurable outcomes across complex, multi-workstream implementations
- Partner with customer executives and senior technical leadership to define platform strategy, develop long-term roadmaps, and align Snowflake capabilities to business objectives
- Translate ambiguous business problems into well-defined, scalable technical solutions, with clear delivery paths and risk-mitigation strategies
- Serve as the trusted escalation point for technical challenges within engagements, unblocking delivery teams and resolving architectural issues
Technical Architecture and Delivery
- Architect and implement end-to-end AI/ML solutions on Snowflake, setting standards for scalability, performance, security, and operability across the customer's environment
- Define and champion MLOps practices within customer organizations, covering model deployment pipelines, monitoring, governance frameworks, and lifecycle management
- Drive adoption of Snowflake's AI product suite (Cortex, Streamlit in Snowflake, Snowflake Intelligence) through architecture leadership and hands-on delivery
- Lead replatforming efforts for complex AI/ML workloads onto Snowflake, coordinating across customer engineering, data science, and platform teams
Cross-Functional Influence
- Act as a bridge between SD delivery, Go-to-Market, and Snowflake product teams, channeling customer feedback to influence product direction
- Mentor and provide technical direction to junior architects and consultants within engagements
- Contribute to the development of reusable architecture patterns, reference implementations, and delivery accelerators for the broader PD organization
OUR IDEAL SENIOR TECHNICAL ARCHITECT WILL HAVE:
- BA/BS in computer science, engineering, mathematics, or a related field, or equivalent practical experience
- 8+ years of experience in solutions architecture, technical consulting, data engineering, or a senior customer-facing technical role
- Demonstrated track record leading architecture decisions on large-scale, enterprise data and AI platforms
- Deep hands-on experience implementing Snowflake in production environments, including data modeling, performance tuning, security design, and platform governance
- Expert-level understanding of the full data analytics stack, from ETL and data pipelines to data platform architecture, BI tooling, and semantic layers
- Strong grasp of the AI/ML lifecycle, from data preparation and feature engineering through model training, deployment, monitoring, and ongoing governance
- Proficiency in SQL and Python; ability to produce and revi
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