Principal Data Platform Engineer

Principal Data Platform Engineer

📍 Location: Tustin, CA, US

(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.

[[JD_HEADING: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

[[JD_HEADING: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

[[JD_HEADING: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

[[JD_HEADING: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

[[JD_HEADING: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.

[[JD_HEADING: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