We are looking for a Senior Data Architect with deep experience in data warehousing, data infrastructure, and large-scale data processing systems. This role focuses on designing and improving the architecture behind data pipelines, with a strong emphasis on automation and AI-driven engineering. The ideal candidate has strong experience designing data warehouse solutions and understands how data moves from multiple sources through ingestion, transformation, validation, and storage. They should also be excited about how AI and automation can change traditional data engineering practices. What You'll Be Doing Define and evolve data architecture for modern data warehouses, including data ingestion, transformation, storage, and processing frameworks. Design scalable and reliable data pipeline architectures that support data from different sources and formats. Establish standards and best practices for ETL/ELT processes, data modeling, data lineage, schema evolution, and change management. Work with SQL, Snowflake, Python, and Apache Airflow to guide and validate the implementation of data architecture. Oversee the architecture and quality of AI-generated pipeline code, ensuring it meets engineering standards and produces reliable results. What We're Looking For Extensive experience in data engineering and data architecture, particularly in designing and maintaining data warehouse solutions. Strong understanding of modern data warehouse architecture, data modeling, data integration, and data processing patterns. Hands-on experience with Snowflake or comparable cloud data warehouse platforms, along with advanced SQL skills. Strong understanding of ETL/ELT frameworks, pipeline orchestration, and tools such as Apache Airflow. Working knowledge of Python for data engineering, automation, and testing. Experience establishing data quality controls, automated testing strategies, data lineage, and change management practices. Ability to make architectural decisions, troubleshoot complex data processing issues, and establish standards that can be applied across multiple pipelines. Familiarity with modern development workflows, including Git, VS Code, and AI-assisted coding tools.