Data Architect; P4642

Overview

84.51° is a retail data science, insights and media company. We help The Kroger Co., consumer packaged goods companies, agencies, publishers and affiliates create more personalized and valuable experiences for shoppers across the path to purchase.

Powered by cutting-edge science, we utilize first-party retail data from more than 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer-centric journey using 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.

84.51° follows a 5‑day in‑office work schedule to support collaboration, alignment, and team connection.

• Design enterprise data architectures for the KPM portfolio, including data modeling, integration patterns, pipeline design, and cloud-native storage strategies that are understandable to both technical and non-technical audiences.
• Define and govern the semantic layer: the business-friendly interface between complex data models and AI-powered applications, enabling natural language querying, agentic AI workflows, and self-service analytics against well-defined, governed data abstractions.
• Architect AI-ready data platforms that support both transactional and analytical workloads, with an emphasis on data product design, conformed dimensions, and patterns that accelerate AI and ML development (feature engineering, model training, and inference serving).
• Guide technical decision-making with engineering and data science teams on architectural trade-offs: build vs. buy, technology selection, data model design, and platform evolution.
• Develop reference architectures and reference implementations, including rapid prototypes, that establish consistent patterns across data engineering, ML pipelines, and AI systems.
• Implement and evolve data governance frameworks, applying established organizational standards to AI systems, including model access control patterns, cost attribution strategies, data lineage, and guardrails that ensure AI systems are secure, compliant, and auditable within the enterprise data perimeter.
• Partner with Data Scientists, ML Engineers, Product Managers, and Engineering teams to ensure the data platform strategy delivers against requirements, scope, and timelines.
• Assess current state and plan for future state aligned with organizational objectives, including migration roadmaps from legacy batch pipelines to modern cloud-native platforms.
• Mentor data engineering and AI platform teams on architectural thinking, data modeling principles, and best practices for building production-grade data systems.
• Evaluate and adopt emerging technologies, including managed AI platforms, semantic layer tooling, and agentic AI frameworks, to improve platform capabilities and developer productivity.
• Ensure security and compliance by partnering with security teams to validate that proposed architectures adhere to enterprise best practices and data governance requirements.
• Participate in organization-wide technology direction as a data domain stakeholder, including Architecture Review Board (ARB) engagements and cross-functional architecture alignment.

• Bachelor s Degree or higher in a field related to software development, technology, or engineering or a related field required.
• 7+ years of experience in engineering organizations, with at least 4+ years in a data architecture or technical leadership role.

• Deep expertise in data architecture principles: dimensional and relational data modeling, data integration patterns, and pipeline architecture (batch and streaming).
• Strong understanding of cloud-native data platforms and services, including Azure Data Lake Storage, Databricks Workflows, Delta Lake, and Azure cloud services broadly.
• Demonstrated ability to design semantic layers and data abstraction patterns that serve both analytical and AI/ML consumers.
• Experience architecting AI-ready data platforms, including data product design, conformed dimensions, and patterns that support feature engineering, model training, and agentic AI workflows.
• Direct experience with…