Principal Data Platform Engineer at Ventura Foods

Job description

Employment Type: Salaried Work Arrangement: Hybrid Position Summary: 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. Major Duties and Responsibilities: • 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 and Experience: 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. Knowledge and Skills: • 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 operationalize AI models. • Security, Compliance & Governance – In-depth knowledge of data security, privacy, and governance frameworks, including RBAC/ABAC models and regulatory compliance. • Leadership & Communication – Strong ability to translate complex technical concepts into business value, influence senior executives, and guide cross-functional teams. • Strategic Thinking – Ability to balance long-term platform vision with short-term delivery, ensuring scalability, adaptability, and cost-effectiveness. Why Join Us: Ventura Foods innovates and manufactures food solutions for foodservice and retail businesses. We make exclusive products for the world's most iconic restaurants and retailers, we provide ready-to-go product solutions for professional kitchens, and we make consumer brands everyone knows and loves. When you work for Ventura Foods, you get a strong foundation of training, a manager who cares about you and celebrates your success, a safe environment, and challenging work. As part of our team, your future is limited only by how much you’re willing to push yourself to get there. We invest in your growth because you invest in ours. Ventura Foods offers career growth opportunities as well as competitive compensation and benefits: • Medical, Prescription, Dental, & Vision – coverage beginning on your 1st day for eligible employees • Profit Sharing and 401(k) matching (after eligible criteria is met) • Paid Vacation, Sick Time, and Holidays • Employee Appreciation Events and Employee Assistance Programs • Salary Base Range of $150,000 - $200,000* *The “base salary range” provided above is a good faith estimate of what we expect to pay for this position in the specified markets. Ventura Foods reserves the right to pay outside of the given range based on a variety of factors including but not limited to: candidate skills and experience, complexity of the job, budgetary factors, and location/geography. Ventura Foods conducts regular reviews of compensation ranges and therefore reserves the right to alter this range at any given time. Diversity & Inclusion: Our commitment to a diverse and inclusive environment in which all employees are treated with respect is evident in our company culture and values. We believe that fostering an environment of inclusion and a focus on diversity across our organization is vital to attracting top talent, driving innovation, and meeting the high expectations of our customers in a rapidly evolving global marketplace. Ventura Foods is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, national origin, disability status, protected veteran status or any other characteristic protected by law.

Requirements

Must-haves

Employment Type: Salaried
Work Arrangement: Hybrid
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.
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.
Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field required.
10+ years of experience in data architecture, data engineering, or enterprise data platforms.

Nice-to-haves

Master’s degree in Computer Science, Information Management, or Business/Technology discipline preferred.

Application prep

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Keywords to mirror

machine learningaiapimlgo

Platform: successfactors
Effort: medium

Benefits

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Dental

Vision

401(K)