Sr. Technical Architect

Responsibilities

• 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.
• Architect and implement end-to-end AI/ML solutions on Snowflake, setting standards for scalability, performance, security, and operability.
• 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.
• 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.

Requirements

• 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 review production-quality code as part of delivery leadership.
• Experience designing and implementing MLOps frameworks and model lifecycle management at enterprise scale.
• Proven ability to influence senior technical and executive stakeholders and navigate complex organizational dynamics.

Nice-to-Haves

• Hands-on experience with generative AI and large language model (LLM) use cases in production.
• Experience building and scaling a Center of Excellence or establishing architectural standards across a large enterprise.
• Background in the services organization of a technology product company.
• AWS, Google Cloud, or Microsoft Azure Advanced certification(s).
• Snowflake SnowPro Advanced Certification(s).
• Deep industry vertical expertise (e.g., Financial Services, Healthcare, Media and Entertainment).
• Experience as a technical lead on multi-team or multi-vendor delivery programs.