Sr. Data Architect

Primary Responsibilities

• Central data architecture: Design and evolve scalable data models and foundational data structures that support analytics, reporting, and AI initiatives used by data products, data practitioners, and data consumers.
• Data normalization: Transform siloed source-system data into standardized, cohesive, business-friendly datasets that can be easily understood and consumed by technical and non-technical users.
• Semantic layer development: Design and maintain semantic models, business definitions, metrics, hierarchies, and context layers that ensure consistent interpretation of data across reporting and AI platforms.
• Data platform expansion: Expand the centralized data platform by identifying, prioritizing, and onboarding new data sources and incorporating additional business systems and datasets into the shared data model.
• AI data readiness: Prepare enterprise data assets for AI and advanced analytics use cases through improved structure, metadata management, contextual relationships, documentation, quality controls, and governance practices.
• Snowflake governance and access design: Develop scalable frameworks for Snowflake role design, access controls, and data-sharing models that balance usability, security, cost tracking, and long-term maintainability.
• Data quality frameworks: Define standards and controls that improve trust, consistency, accuracy, completeness, and reliability across enterprise data assets.
• Platform scalability: Identify opportunities to reduce complexity, eliminate redundancy, improve maintainability, and support long-term growth of enterprise data assets.
• Documentation and standards: Create and maintain technical documentation, data dictionaries, architecture diagrams, semantic definitions, governance artifacts, and development standards.

Key Knowledge, Skills and Abilities Required

• Bachelor's degree in Computer Science, Engineering, Data Analytics, Information Systems, or a related technical field.
• 5+ years of experience in data architecture, data engineering, analytics engineering, business intelligence, or related disciplines.
• Strong understanding of data modeling, data architecture principles, and data governance practices.
• Strong understanding of semantic layer and context layer frameworks.
• Advanced SQL skills and experience working with modern cloud data platforms.
• Experience designing scalable and maintainable data structures that support reporting, analytics, and AI use cases.
• Strong understanding of metadata management, master data concepts, role-based access controls, and enterprise data standards.
• Demonstrated ability to simplify complex information and communicate technical concepts effectively to business stakeholders.
• Strong analytical, documentation, communication, and problem-solving skills.

Desired

• Experience with Snowflake and dbt.
• Experience developing semantic layers, governed metrics, or enterprise reporting frameworks.
• Familiarity with data governance frameworks, data catalog solutions, or stewardship programs.
• Experience supporting AI and machine learning use cases through data preparation and architecture.
• Experience within renewable energy, manufacturing, field service, or asset-intensive industries.
• Relevant Snowflake, Microsoft, DAMA, or data architecture certifications.