Senior Technical Architect

🕒 Ontem

🏄 California – Remoto

🏄 California – Remote

💵 $160.000 - $210.000 / ano

⏰ Tempo Integral

🟠 Sênior

🔙 Engenheiro Backend

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Snowflake

SiteLinkedInTodas as Vagas

5001 - 10000 funcionários

Fundada em 2012

💼 Consultoria

📣 Marketing

Consulting • Marketing • Cloud Computing

A Snowflake é uma empresa de data warehousing em nuvem que oferece uma plataforma inovadora para armazenamento, processamento e analytics de dados. Ela permite que as organizações lidem com grandes volumes de dados de forma eficiente, facilitando insights e Business Intelligence. A arquitetura da Snowflake suporta diversas cargas de trabalho de dados, tornando-a uma solução versátil para empresas que buscam alavancar seus dados na tomada de decisões estratégicas.

Descrição

• 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 • 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

🎯 Requisitos

• 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 • 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.

🏖️ Benefícios

• Health insurance • Professional development opportunities • Flexible working hours