Senior Data Cloud Architect

Role Overview

• Strategic Account Alignment with top accounts for SI partners (Focus on Acceleration):
• Unlock High-Impact Customer Journeys: Identify and prioritize AI/ML customer journeys for SI partner top accounts, focusing on quick wins and high consumption, informed by your understanding of SI partner needs.
• Expedited POCs and RFPs: Provide support for partners during RFI/RFP processes and POCs, ensuring rapid turnaround and successful outcomes, informed by your SI proposal experience.
• Rapid Deployment Frameworks: Develop and share best practices and POC frameworks specifically designed to accelerate the deployment of prioritized AI/ML customer journeys, based on your SI project implementation knowledge.
• SI Partner Snowflake Practice Building (Focus on Production Readiness):
• COE for Rapid Implementation: Collaborate on creating and managing Partner Snowflake Centers of Excellence (COE) with a focus on accelerating AI/ML deployments, leveraging your SI center of excellence experience.
• Accelerated Capability Maturity: Drive partner capabilities maturity for each AI/ML product category, emphasizing hands-on skills and practical knowledge, informed by your SI skill development experience.
• Targeted Communication: Deliver public webinars and product roadmap sessions focused on accelerating AI/ML implementation, drawing on your SI presentation and communication experience.
• Work on Initiatives and Joint Solutions with SI partners (Focus on Market-Ready Solutions):
• Rapid Joint Solution Development: Build or replatform Partner-led Joint Industry Solutions for AI/ML, focusing on speed to market, based on your SI solution development experience.
• Asset Creation for Fast Implementation: Assist partners with asset creation for AI/ML product categories, focusing on templates, code samples, and deployment guides, drawing on your SI asset creation experience.
• Customer Journey Alignment for Quick Wins: Align AI/ML customer journeys with Partner Solutions/Offerings, focusing on immediate value and quick wins, informed by your SI customer journey analysis experience.
• AI/ML Focused work with SI partners (Hands-On Expertise):
• Enable partners to leverage Cortex AI for gen AI customer journeys, including Cortex LLM, Fine Tuning, Search, and Analyst, with a focus on practical application.
• Support partners in implementing end-to-end ML development and MLOps using Snowflake ML, emphasizing rapid deployment and operationalization.
• Advise partners on using Cortex AI for unstructured data analytics, focusing on real-world customer journeys and quick results.
• Mentor and guide junior cloud architects in our PSE team working on critical AI/ML engagements with our SI partners, aiming to accelerate their customer journeys, ensuring production readiness, and developing market-ready solutions. By sharing expertise and providing hands-on guidance, the Principal Data Cloud Architect will empower the junior team members to effectively support SI partners in deploying successful AI/ML solutions on the Snowflake AI Data Cloud.

Requirements

• REQUIRED: Experience working at a large System Integrator (SI), with a proven track record of accelerating technical wins and production deployments.
• PLUS: Knowledge of competitive AI/ML platforms and solutions.
• Extensive experience with cloud data platforms, preferably Snowflake.
• Hands-on experience with Cloud ML platforms such as Sagemaker, Azure ML, VertexAI, and/or MLFlow.
• Experience building and deploying ML model pipelines using serverless and containerized computing including AWS Lambda, Azure Functions, Google Cloud Functions, Docker, Kubernetes etc.
• Familiarity with MLOps and CI/CD processes and tools, such as GIT, Azure DevOps, Sagemaker Pipelines, Google Cloud Build
• Experience with developing AI/ML use cases including communicating AI/ML strategy and business value
• Experience using Big Data or Cloud integration technologies such as Matillion, Azure Data Factory, AWS Glue, AWS Lambda, etc.
• Experience in developing and deploying Machine Learning models through the full Data Science life cycle.
• Expertise in data science programming languages including Python, SQL, Scala, and/or Spark.
• Strong experience with major cloud platforms and tooling, especially Azure, AWS, GCP
• Strong CS fundamentals, including proficiency with data structures, algorithms, and distributed systems.
• 10+ years industry experience designing, building and supporting large-scale systems with a minimum of 5 years in a pre-sales role.
• BS/MS/PhD in Computer Science or related majors.

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Docker
• Google Cloud Platform
• Kubernetes
• Matillion
• Python
• Scala
• Spark
• SQL

Benefits

• No benefits listed