Principal Data Platform Engineer
📍 Location: Irvine, California, United States (Hybrid)
🏢 Industry: Food and Beverage Services
💼 Work Setting: Hybrid

Are you a senior data architecture leader with deep Snowflake expertise, enterprise integration experience, and a vision for scalable data ecosystems? This role offers the opportunity to architect a modern cloud-native lakehouse platform, enable self-service analytics, establish governance standards, and build the data foundation for AI-driven innovation.
As the Principal Data Platform Architect, you will partner with business executives, engineering leaders, analytics teams, and data scientists to ensure enterprise data assets are trusted, secure, scalable, and aligned with strategic business objectives.

Key Responsibilities
Enterprise Data Platform Strategy
Platform Vision & Roadmap
Define and own the enterprise data platform strategy.
Develop a long-term roadmap for:
Data Platforms
Integration Architecture
Analytics Ecosystems
AI & Machine Learning Enablement
Strategic Alignment
Align platform capabilities with evolving business priorities.
Support strategic objectives including:
Business Growth
Operational Efficiency
Regulatory Compliance
Innovation
Digital Transformation
Architecture Governance
Establish enterprise architecture standards and principles.
Define governance frameworks and best practices.
Ensure consistency across enterprise data initiatives.
Snowflake Lakehouse Architecture
Enterprise Lakehouse Design
Medallion Architecture
Design and govern enterprise data architecture utilizing:
Bronze Layer (Raw Data)
Silver Layer (Curated Data)
Gold Layer (Business-Ready Data)
Data Modeling
Define logical and physical enterprise data models.
Support analytics, operational reporting, and AI workloads.
Enable scalable and reusable data structures.
Platform Optimization
Optimize performance and scalability.
Drive cost management initiatives.
Design multi-tenant architectures supporting enterprise growth.
Data Quality & Trust
Ensure:
Data Quality
Data Lineage
Metadata Management
Data Observability
Data Consistency
Across structured and semi-structured datasets.
Enterprise Integration Architecture
Integration Strategy
Data Movement Frameworks
Establish enterprise standards for:
API-Led Integrations
Event-Driven Architecture
Batch Processing
Real-Time Data Pipelines
Enterprise Connectivity
Enable interoperability between:
Enterprise Applications
Data Warehouse Platforms
Operational Systems
Cloud Services
Analytics Platforms
Operational Monitoring
Define monitoring and observability standards.
Implement metadata capture and lineage tracking.
Standardize error handling and recovery mechanisms.
Analytics & Self-Service BI
Business Intelligence Enablement
Self-Service Analytics
Design frameworks that empower business users through:
Governed Data Access
Trusted Datasets
Scalable Analytics Architecture
Semantic Layer Architecture
Build enterprise semantic models.
Create reusable business definitions and metrics.
Improve reporting consistency across business units.
Data Governance for Analytics
Define standards for:
Certified Analytics
Exploratory Analysis
Data Ownership
Data Stewardship
Data Cataloging
Develop frameworks for:
Data Catalogs
Data Dictionaries
Business Glossaries
User Documentation
AI & Machine Learning Platform Enablement
AI Data Foundation
Machine Learning Readiness
Ensure data platforms support:
Feature Engineering
Model Training
Model Validation
Model Deployment
MLOps Collaboration
Partner with Data Science and ML Engineering teams to:
Establish MLOps standards
Improve model operationalization
Enable scalable machine learning workflows
AI Innovation
Support data reuse for:
Predictive Analytics
Generative AI
Intelligent Automation
Advanced Analytics Applications
Feature Management
Contribute to architecture supporting:
Feature Stores
AI Data Pipelines
Model Monitoring
AI Governance
Data Governance, Security & Compliance
Data Protection
Design enterprise controls supporting:
Data Privacy
Data Security
Regulatory Compliance
Risk Management
Security Architecture
Implement and govern:
Role-Based Access Control (RBAC)
Attribute-Based Access Control (ABAC)
Data Classification
Encryption Policies
Regulatory Compliance
Ensure adherence to internal and external compliance standards.
Support audit and governance requirements.
Leadership & Stakeholder Engagement
Executive Partnership
Collaborate with:
Executive Leadership
Business Leaders
Data Teams
IT Leadership
Analytics Stakeholders
Product Teams
Strategic Consultation
Translate technical capabilities into business value.
Influence enterprise data strategy and investment decisions.
Drive adoption of data-driven decision-making practices.
Cross-Functional Leadership
Guide architects, engineers, and analysts.
Establish technical standards and best practices.
Foster collaboration across business and technology teams.

Required Qualifications
Education
Bachelor's Degree in:
Computer Science
Information Systems
Data Engineering
Related Technical Discipline
Preferred
Master's Degree in:
Computer Science
Information Management
Business Technology
Related Field
Experience
Data Architecture
10+ years of experience in:
Data Architecture
Data Engineering
Enterprise Data Platforms
Architecture Leadership
5+ years in senior architecture leadership roles.
Experience driving enterprise strategy, governance, and standards.
Cloud Data Platforms
Proven success designing cloud-native data platforms.
Extensive experience with Snowflake or similar modern platforms.
Enterprise Analytics
Experience enabling enterprise self-service BI environments.
Strong background in data governance and analytics architecture.
AI & Machine Learning
Exposure to ML platforms, MLOps practices, and AI enablement strategies.

Technical Skills
Data Architecture
Lakehouse Architecture
Medallion Architecture
Enterprise Data Modeling
Data Governance
Metadata Management
Cloud Data Platforms
Snowflake
Multi-Tenant Design
Performance Optimization
Cost Management
Data Security
Data Engineering
dbt
ELT / ETL Frameworks
Data Pipeline Automation
Data Orchestration
Data Transformation
Integration Technologies
REST APIs
Event-Driven Architecture
Streaming Integration
Batch Processing
Enterprise Integration Patterns
Analytics & BI
Self-Service Analytics
Semantic Layer Design
Data Cataloging
Reporting Platforms
Business Intelligence Frameworks
AI & Machine Learning
MLOps
Feature Engineering
Feature Stores
Model Operationalization
AI Platform Architecture

Key Skills
Snowflake
Data Platform Architecture
Lakehouse Architecture
Data Engineering
Enterprise Integration
Data Governance
Self-Service BI
Data Modeling
dbt
MLOps
AI Enablement
Cloud Data Platforms

Core Competencies
Strategic Thinking
Enterprise Architecture Leadership
Innovation
Business Acumen
Communication
Stakeholder Management
Technical Vision
Problem Solving
Governance
Cross-Functional Collaboration

Principal Data Platform Engineer

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