The Principal Data Platform Architect defines and drives the company’s enterprise data platform strategy and roadmap. This role designs a modern lakehouse architecture on Snowflake, enables self-service BI, advances integration across systems and data domains, and establishes the foundation for machine learning and AI platforms—ensuring the ecosystem is scalable, secure, and delivers trusted data for business value.

• Define and own the enterprise data platform strategy and roadmap:
  • Develop the architectural vision for data, integration, analytics, and AI platforms.
  • Align platform capabilities with evolving business needs such as growth, efficiency, compliance, and innovation.
  • Establish architectural principles, standards, and governance.
• Design and govern Snowflake-based lakehouse architecture (medallion model):
  • Design the logical data models for bronze, silver, and gold layers.
  • Ensure data quality, lineage, and security across structured and semi-structured data.
  • Optimize performance, cost management, and multi-tenant scalability.
• Lead integration architecture across systems and data domains:
  • Define patterns for API-led, event-driven, and batch data movement.
  • Standardize error handling, monitoring, and metadata capture.
  • Ensure interoperability between enterprise applications, data warehouse, and operational platforms.
• Enable and expand self-service BI and analytics:
  • Architect semantic layers and governed datasets for analytics platforms.
  • Define policies for certified vs. exploratory analytics.
  • Deliver frameworks for data catalogs, data dictionaries, and end-user guides.
• Lay the foundation for machine learning and AI platforms:
  • Ensure data pipelines support ML feature engineering and model training.
  • Partner with data science teams to establish MLOps best practices.
  • Enable reuse of data assets for predictive and generative AI use cases.

Education

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field required.
• Master’s degree in Computer Science, Information Management, or Business/Technology discipline preferred.

Experience

• 10+ years of experience in data architecture, data engineering, or enterprise data platforms.
• 5+ years in a senior/lead architect role driving platform strategy, standards, and governance at enterprise scale.
• Proven expertise designing and operating cloud-based data platforms (Snowflake or similar), including lakehouse/medallion architectures.
• Hands-on experience with ELT/ETL tools, data modeling, and metadata management (dbt, Fivetran, or comparable).
• Strong background in enterprise integration (API-led, event-driven, or batch patterns).
• Demonstrated success enabling self-service BI and analytics platforms with governed semantic layers.
• Exposure to machine learning/AI platforms and MLOps practices for operationalizing models.
• Track record of partnering with business leaders to align data platforms with strategic business needs (growth, efficiency, compliance, innovation).
• Excellent leadership, communication, and stakeholder management skills; experience influencing technical and executive audiences.

• Data Architecture & Modeling – Expert in designing lakehouse architectures (bronze/silver/gold) and enterprise data models that support both analytics and AI/ML workloads.
• Cloud Data Platforms – Deep hands-on knowledge of Snowflake (or equivalent cloud-native platforms), including performance tuning, cost optimization, multi-tenant design, and governance.
• Data Engineering & ELT – Strong proficiency in dbt or similar transformation frameworks, ELT/ETL orchestration, metadata management, and automation of data pipelines.
• Integration Patterns – Advanced understanding of API-led, event-driven, and batch integration; skilled at designing reusable integration frameworks.
• Analytics & BI Enablement – Skilled at architecting self-service BI environments, semantic layers, and governed datasets; experience with enterprise BI tools.
• Machine Learning & AI Enablement – Familiarity with ML pipelines, feature stores, and MLOps practices to