Data Warehouse Architect

You are a skilled IT Professional with 4-12 years of experience in Snowflake Data Cloud, and you will be responsible for leading the design, implementation, and optimization of scalable data solutions. Your expertise lies in Snowflake architecture, data warehousing, data lakes, serverless/cloud automation, and AI/LLM concepts. Your main responsibilities will include architecting and delivering end-to-end data solutions on Snowflake, demonstrating a deep understanding of data warehousing concepts, working with different types of data, utilizing Snowflake native load utilities for data ingestion and transformation, developing API-based integrations and automation, leading data governance, quality, security, and compliance efforts, optimizing performance and managing costs, overseeing Snowflake infrastructure provisioning, scaling, and monitoring, driving meetings with stakeholders, fostering innovation in data architecture and analytics, collaborating with cross-functional teams, staying updated on Snowflake advancements, performing POCs for new features, and leveraging data lake frameworks and serverless technologies. To qualify for this role, you should hold a Bachelor's or Master's degree in computer science, IT, or a related field, possess 4-12 years of experience in Snowflake Data Cloud, have a deep understanding of Snowflake architecture and ecosystem, be proficient in data warehousing concepts and diverse data structures, have expertise in Snowflake native load utilities, experience in API integration and automation, a background in data governance, security, and compliance, skills in performance optimization and cost management, familiarity with Snowflake infrastructure management, excellent communication and stakeholder management abilities, an innovative, AI-driven mindset, hands-on experience with Python for data engineering/automation, knowledge of AI, LLMs, and Snowflake AI features, as well as experience with data lake frameworks and serverless technologies. Preferred qualifications include familiarity with data modeling, Python, Pyspark, and warehousing best practices, exposure to MLOps, AI/ML pipelines, or deploying AI models, and Snowflake or relevant cloud certifications.,