Principal Data Warehouse Architect

Seeking an experienced Data Warehouse Principal Architect to lead the end‑to‑end design, implementation, and ongoing ownership of the organization’s enterprise data warehouse and analytics platform with Snowflake as the core platform. This role will manage technical scope, data strategy, data ingestion architecture, delivery roadmap, and partner with stakeholders on reporting and analytics consumption needs.

Phase one focuses on commercial readiness: rapid, trusted integration from key enterprise systems and external data sources, and delivery of scalable reporting and operational analytics. This serves as the foundation for a multi‑year expansion across additional business functions.

The Principal Architect will initially be a sole contributor, leading external consultants and implementation partners while working closely with the IT platform team, cybersecurity, business and technical analysts, business leadership, and executives. The role reports to the Senior Director, Business Systems.

• Lead the full lifecycle implementation of Snowflake, from architecture design and environment setup through production deployment and ongoing operations.
• Design and build a right‑sized data ingestion solution for current and future needs, selecting appropriate tooling, pipeline architecture, and ELT/ETL patterns for batch and streaming sources.
• Define Snowflake environment standards: database/schema design, virtual warehouse sizing, resource monitoring, cost optimization, and access control frameworks.
• Architect and implement staging, raw vault, and consumption layers to support trusted analytics across business functions.
• Establish production operating practices including monitoring, alerting, data observability, performance tuning, and SLAs for data availability and quality.
• Lead cross‑functional sessions to define priorities, clarify requirements, resolve trade‑offs, and drive timely decisions.
• Lead external consultants, system integrators, and vendors, defining scope, deliverables, reviewing work products, and ensuring solutions meet standards and business outcomes.
• Define and evolve the data platform operating model, support processes, documentation standards, and scaling plans for additional capabilities and partner support.
• Collaborate directly with business and technical users to gather, clarify, and prioritize reporting and analytics requirements, translating needs into scalable, reusable data models and reporting‑layer solutions.
• Define and build semantic, logical, and physical data models for self‑service analytics, standard dashboards, and operational workflows.
• Partner with analytics and BI teams to ensure the platform supports tools such as Tableau, Power BI, or similar, delivering consistent, trusted metrics.
• Architect systems supporting analytical, operational, and regulatory workloads using batch and real‑time data patterns.
• Define and implement scalable metric definitions and KPI frameworks for commercial and enterprise reporting requirements.
• Evaluate, select, and implement the right‑sized data ingestion solution, balancing capability, cost, and operational complexity against projected volumes and source system diversity.
• Design and build ingestion pipelines from key enterprise systems (CRM, ERP, LIMS, external data feeds, etc.) with reliability, lineage, and auditability.
• Define standards for ingestion tooling, configuration management, error handling, retry logic, and schema evolution to support platform maintainability.
• Ensure ingestion architecture supports incremental loads, CDC, and real‑time patterns where appropriate.
• Define and maintain the enterprise data architecture roadmap aligned with business and technology strategy.
• Develop reference architectures, design patterns, and reusable components for ingestion, transformation, modeling, and analytics.
• Define enterprise standards and governance frameworks across data assets and platform components.
• Define and support master and reference data strategies to ensure consistency of critical enterprise data.
• Embed data quality, metadata, and lineage considerations into all designs.
• Partner with governance and stewardship functions to…