Senior Technical Architect
Leads complex customer engagements involving Snowflake data and AI platforms, owning architecture, delivery outcomes, and executive advisory. Requires 8+ years of technical architecture or consulting experience, deep production Snowflake expertise, and strong AI/ML and MLOps skills.
About the job
Responsibilities
- Lead customer engagements as the primary technical authority for complex, multi-workstream implementations.
- Partner with executives and senior technical leaders to define platform strategy, roadmaps, and business alignment.
- Translate ambiguous business problems into scalable technical solutions and delivery plans.
- Resolve architectural issues and serve as the technical escalation point for engagements.
- Architect and implement end-to-end AI/ML solutions on Snowflake, with standards for scalability, performance, security, and operability.
- Define and champion enterprise MLOps practices, including deployment pipelines, monitoring, governance, and lifecycle management.
- Drive adoption of Snowflake Cortex, Streamlit in Snowflake, and Snowflake Intelligence through architecture leadership and hands-on delivery.
- Lead replatforming of complex AI/ML workloads onto Snowflake across engineering, data science, and platform teams.
- Bridge Services Delivery, Go-to-Market, and product teams while channeling customer feedback into product direction.
- Mentor junior architects and consultants.
- Develop reusable architecture patterns, reference implementations, and delivery accelerators.
Requirements
- Bachelor's degree 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.
- Experience leading architecture decisions for large-scale enterprise data and AI platforms.
- Deep production experience with Snowflake, including data modeling, performance tuning, security design, and platform governance.
- Expert understanding of the data analytics stack, including ETL, data pipelines, data platforms, BI tooling, and semantic layers.
- Strong understanding of the AI/ML lifecycle, including data preparation, feature engineering, training, deployment, monitoring, and governance.
- Proficiency in SQL and Python, including production-quality code review.
- Experience designing and implementing enterprise-scale MLOps frameworks and model lifecycle management.
- Ability to influence senior technical and executive stakeholders and navigate complex organizational dynamics.
Nice-to-haves
- Production experience with generative AI and large language model use cases.
- Experience building a Center of Excellence or establishing enterprise architectural standards.
- Experience in the services organization of a technology product company.
- Advanced certification in AWS, Google Cloud, or Microsoft Azure.
- Snowflake SnowPro Advanced Certification.
- Deep industry expertise in financial services, healthcare, or media and entertainment.
- Experience leading multi-team or multi-vendor delivery programs.
Skills
Snowflake, AI/ML, MLOps, SQL, Python, Data Modeling, ETL, Data Pipelines, Bi Tooling, Semantic Layers, Feature Engineering, Generative AI, LLMs, AWS, GCP
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