Principal Data Platform Engineer
📍 Location: Irvine, California, United States (Hybrid)
🏢 Industry: Food and Beverage Services
💼 Work Setting: Hybrid
Are you passionate about building scalable data platforms, driving data strategy, and enabling advanced analytics and AI capabilities across the enterprise? We are seeking an experienced Principal Data Platform Architect to lead the vision, design, and evolution of a modern enterprise data ecosystem. In this strategic leadership role, you will define data architecture standards, establish platform governance, and create a robust foundation that empowers data-driven decision-making, self-service analytics, machine learning, and future innovation.
You will collaborate with business leaders, technology teams, data engineers, analysts, and architects to ensure the organization's data platforms are scalable, secure, reliable, and aligned with long-term business objectives.
Key Responsibilities
Enterprise Data Strategy & Architecture
Define and own the enterprise data platform strategy, architecture roadmap, and governance framework.
Establish architectural principles, standards, and best practices across data, analytics, integration, and AI platforms.
Align data platform capabilities with business priorities, operational goals, compliance requirements, and innovation initiatives.
Lead the evaluation and adoption of emerging technologies that enhance enterprise data capabilities.
Data Platform & Lakehouse Architecture
Design and govern modern cloud-based data platform architectures that support enterprise-scale analytics and reporting.
Develop logical and physical data models that enable efficient data processing, storage, and consumption.
Establish frameworks to ensure data quality, lineage, cataloging, governance, and security across structured and unstructured datasets.
Drive platform scalability, performance optimization, reliability, and cost efficiency.
Data Integration & Engineering
Define enterprise integration patterns for real-time, event-driven, API-based, and batch data movement.
Establish standards for data ingestion, transformation, orchestration, monitoring, and metadata management.
Ensure seamless interoperability between business applications, data repositories, operational systems, and analytics platforms.
Promote reusable integration frameworks and automation practices.
Analytics & Self-Service Enablement
Architect governed data environments that support business intelligence, reporting, and advanced analytics.
Define standards for certified, trusted, and self-service analytics datasets.
Enable enterprise data accessibility through semantic models, curated data products, catalogs, and documentation.
Partner with stakeholders to improve data literacy and adoption across the organization.
AI, Machine Learning & Data Innovation
Establish data architecture patterns that support machine learning, predictive analytics, and AI initiatives.
Ensure data pipelines and platforms are optimized for model development, training, deployment, and monitoring.
Collaborate with data science and engineering teams to implement scalable operational frameworks for AI solutions.
Enable reuse of enterprise data assets across analytical and intelligent applications.
Governance, Security & Compliance
Define enterprise standards for data governance, privacy, security, retention, and regulatory compliance.
Ensure appropriate access controls, auditing, monitoring, and stewardship processes are implemented.
Lead initiatives that improve data trustworthiness, consistency, and stewardship across business domains.
Establish policies for data ownership, quality management, and enterprise-wide governance.
Leadership & Stakeholder Management
Partner with business and technology leaders to align platform investments with strategic priorities.
Communicate architectural direction and technology strategy to technical and non-technical stakeholders.
Provide technical leadership, mentorship, and guidance to architects, engineers, and data professionals.
Drive cross-functional collaboration to ensure successful delivery of enterprise data initiatives.
Required Qualifications
Bachelor's degree in Computer Science, Information Systems, Data Engineering, Technology, or a related field.
10+ years of experience in data architecture, data engineering, enterprise data management, or related disciplines.
5+ years of experience in a senior architecture or technical leadership role.
Proven expertise designing and implementing enterprise-scale cloud data platforms and modern data architectures.
Strong experience with data modeling, data warehousing, metadata management, and data integration technologies.
Demonstrated success leading enterprise data transformation and modernization initiatives.
Experience partnering with business and technology leaders to align data strategy with organizational objectives.
Preferred Skills & Experience
Deep expertise in cloud-native data platforms, data lakehouse architectures, and modern analytics ecosystems.
Strong knowledge of ELT/ETL frameworks, data pipeline orchestration, automation, and transformation methodologies.
Experience designing enterprise integration solutions using API, event-driven, and batch processing patterns.
Strong understanding of business intelligence platforms, semantic modeling, and self-service analytics enablement.
Familiarity with machine learning platforms, MLOps practices, and AI-driven solution architectures.
Expertise in data governance, security frameworks, privacy regulations, and access management models.
Strong knowledge of enterprise architecture principles and software development lifecycle practices.
Experience optimizing scalability, performance, reliability, and cost management across enterprise platforms.
Exceptional communication, leadership, stakeholder management, and influencing skills.
Ability to translate complex technical concepts into business value and strategic outcomes

Principal Data Platform Engineer

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