Staff Data Architect

Kraft Heinz•Chicago, IL

6d•Onsite

About The Position

Here at Kraft Heinz, we grow our people to grow our business, because we believe that great people make great companies. When you join our table, you can expect access to an array of holistic wellness benefits and perks, including medical, dental and vision coverage, 7% 401(k) matching, Business Resource Groups (BRGs) to help foster diversity, inclusion, and belonging for all employees, an industry-leading total rewards package that emphasizes a high discretionary bonus. Benefits begin immediately upon hire for salaried employees. Get a peek into life here at Kraft Heinz through our Instagram and TikTok channels! Staff Data Architect at a glance... You will execute the enterprise data architecture strategy that powers the North America Supply Chain's transition to data-driven, predictive decision-making, spanning planning, logistics execution, and customer excellence. You will be successful by build a unified, resilient data foundation that reduces time-to-insight and supports supply chain agility, directly advancing Kraft Heinz's broader goals of operational excellence, cost efficiency, and growth through digital transformation.

Requirements

Years of relevant experience in design and implementation of scalable, high-performance, and reusable data models that help in generating actionable insights.
Strong background in data warehouse / lakehouse implementation
Experience in designing data models (Logical, Physical, Conceptual ERD) using data modeling tools (ERWIN, ER/STUDIO, etc.).
Ability to explain complex technical concepts clearly to non-technical executive stakeholders.
Demonstrated ability to translate business requirements into technical solutions driving data architecture and Data Product design.
Experience with enterprise-scale data engineering orchestration frameworks/ELT tools and common data engineering Python libraries (dbt, pandas, great expectations, etc.).
Experience designing semantic models/Semantic Views for BI/AI data requirements, and familiarity with Snowflake Cortex (Cortex Analyst, Cortex Search, Cortex Agents) or comparable conversational-AI/agent platforms
Understanding of what it takes to make data “AI-ready”—data quality, metadata, governance, and access patterns suited to LLM, ML, and agentic consumption
Knowledge of Agile methodologies with experience working on tools such as Jira & Confluence.
Growing understanding of business processes and functional understanding of ERP tools like SAP, OTM, O9, OMP etc. with an analytical background.

Responsibilities

Execute the organization’s Data Architecture Strategy, data lifecycle framework, and standards—covering data ingestion, modelling, security, consumption, storage and retention—for the Supply Chain function, in partnership with the Data Architecture Leads
Lead the design of source and insight data products, global data models and semantic models, ensuring optimal performance for BI, AI and conversational analytics
Design and build Snowflake Semantic Views that encode governed business metrics, dimensions, and relationships, giving BI tools and AI agents a single, trusted definition of the truth
Partner with Data Engineering to curate AI-ready data products—trusted, well-documented, quality-assured data suited for consumption by GenAI, ML, and agentic applications—and help define what “AI-ready” means for the Supply Chain domain
Design and configure Cortex Agents (orchestrating Cortex Analyst over Semantic Views, and Cortex Search over unstructured content) to power conversational and agentic analytics for Supply Chain stakeholders
Work with business stakeholders, product owners, functional leads and cross-functional chapters to design and develop data products and solutions within the North America Supply Chain function, supporting analytical and decision intelligence use cases across a large variety of internal and external data sources
Translate business requirements into scalable, high-performance and reusable data models that can be efficiently utilized across different data initiatives and generate actionable insights
Design and implement comprehensive lakehouse architecture incorporating robust data access controls and security