Data Architect - Snowflake

Introduction A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences. Your Role And Responsibilities We are seeking a skilled Snowflake Data Architect with strong hands‑on expertise in Snowflake, advanced SQL, and modern data architecture practices. The ideal candidate will design scalable data models, optimize data pipelines, and guide best practices for enterprise data platforms. Key Responsibilities

• Design and maintain scalable data architectures using Snowflake
• Develop advanced SQL solutions for data transformation, analytics, and performance optimization
• Create and manage dimensional data models following Kimball principles
• Oversee ETL and ELT data pipeline design and implementation
• Establish and enforce data architecture standards, best practices, and governance
• Partner with data engineering, analytics, and business teams to ensure high‑quality data solutions
• Support version control and development workflows using Git
• Implement and optimize CI/CD processes for data platform releases
• Drive architectural improvements, including performance tuning and optimization of Snowflake environments

Preferred Education Master's Degree Required Technical And Professional Expertise

• Hands‑on experience with Snowflake architecture, performance tuning, and data engineering features
• Advanced SQL skills
• Strong background in data modeling, including dimensional modeling and Kimball methodology
• Experience with ETL and ELT tools and frameworks
• Practical experience with Git for version control
• Familiarity with CI/CD pipelines for data engineering or analytics environments
• Strong understanding of scalable data platform design and best practices

Preferred Technical And Professional Experience

• Experience with cloud platforms such as AWS, Azure, or GCP
• Exposure to data governance and metadata management tools
• Knowledge of data orchestration tools such as Airflow, ADF, or dbt Cloud
• Understanding of modern data quality and testing frameworks