• Define and maintain KSM’s Databricks lakehouse architecture, including ingestion, storage, transformation, modeling, and access patterns • Establish clear, repeatable design standards for Bronze, Silver, and Gold data layers to ensure consistency and reuse • Design architecture that supports structured, semi-structured, and unstructured data across enterprise systems • Design and implement data governance, lineage, and quality frameworks that ensure data is trusted, auditable, and scalable • Enable discoverability, access control, and metadata management using Databricks Unity Catalog and related tooling • Partner with data and analytics teams to define validation standards, reconciliation processes, and ownership models • Partner closely with the Senior Data Engineer to translate architectural standards into production-ready pipelines • Review and validate data pipelines, models, and workflows to ensure alignment with architectural best practices • Support teams by providing guidance, patterns, and examples rather than one-off solutions • Define best practices for Databricks compute and storage optimization, balancing performance and cost • Establish architectural patterns that promote reliability, scalability, and operational simplicity • Collaborate on monitoring, alerting, and operational standards to ensure platform health • Define and support CI/CD standards for data pipelines and lakehouse infrastructure • Partner with engineering teams to implement Infrastructure as Code (IaC) using tools such as Terraform • Ensure consistent, automated deployments across development, test, and production environments • Align data architecture decisions with firm-wide analytics, automation, and AI strategy • Help prioritize architectural investments that deliver near-term value while supporting long-term growth • Evaluate emerging data platform capabilities thoughtfully, focusing on practical adoption rather than experimentation for its own sake.