Data Architect (P4642)

Technical Architect / Data Architect

• The Data Architect plays a key role within our Software Architecture group by leading the design and delivery of modern software solutions that support our commercial products and platforms.

  As a senior individual contributor, you will partner closely with engineering, product, experience design, security, data science, and other cross functional teams to solve complex technical and business problems. You will bring deep expertise in software architecture, cloud native design, and modern engineering practices to help teams make strong technical decisions and deliver high quality solutions. You will also help shape how we work by improving architecture patterns, technical standards, and ways of working across teams.
  
  This role requires strong technical judgment, business acumen, and the ability to influence senior stakeholders. You should be energized by working through ambiguity, evaluating tradeoffs, and creating practical solutions that balance customer needs, business priorities, and long term technical sustainability. Familiarity with ad tech or retail media, the AI ecosystem, and the Azure cloud platform will help you be successful in this role.

  We are seeking a Data Architect to design and help deliver the data foundations that power AI-first products across the Kroger Precision Marketing portfolio. In this role, you will define data architecture patterns that make data accessible, governable, and AI-ready. You will work alongside data scientists, ML engineers, software and data engineers, and product managers to translate complex data requirements into scalable, production-grade architectures that serve clients, internal users and agents.

  As a technical leader, you will establish reference architectures, guide data platform evolution, and architect semantic layers that bridge raw data and intelligent applications. You bring deep expertise in cloud-native data platforms, data modeling, and the architectural patterns required to build and govern AI systems at scale. You are as comfortable whiteboarding a medallion architecture with an engineering team as you are articulating data governance trade-offs to senior leadership.

  What you will do:

  • 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.

  Qualifications, Skills, and Experience:

  • 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.

  Technical Expertise (Required):

  • 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 Databricks OR Snowflake (expertise in one is required; both is a bonus).

  • Proficiency in SQL and at least one data engineering language (Python strongly preferred).

  • Understanding of AI and ML system architecture, including data pipelines for model training, inference serving, and observability.

  • Working knowledge of data governance frameworks: RBAC, data lineage, cost attribution, access control, and audit logging.

  • Experience with monitoring and observability patterns for data platforms (Datadog or equivalent).

  • Experience with cloud cost management for data platforms, including usage monitoring, cost attribution, and spend projection across Databricks and Snowflake environments.

  Analytical and Problem-Solving Skills:

  • Critical thinker with a strong emphasis on identifying, evaluating, and recommending solutions, including build vs. buy analysis and architectural trade-off documentation.

  • Strong problem-solving skills with a proactive approach to technical challenges.

  • Ability to assess current-state architectures and develop actionable, phased migration roadmaps.

  • Experience transitioning systems from legacy batch architectures to event-driven or streaming patterns.

  Business Acumen, Communication and Leadership:

  • Strong business sense with the ability to translate business requirements into scalable technical solutions.

  • Excellent communication skills: ability to convey complex data and AI architecture concepts to both technical and non-technical audiences, including senior leadership.

  • Ability to influence and guide technical teams through expertise and collaborative leadership.

  • Comfort making time-sensitive architectural decisions with incomplete information.

  Desirable Additional Skills:

  • Experience with managed AI platforms operating within an enterprise data perimeter (e.g., Snowflake Cortex AI, Azure OpenAI Service).

  • Familiarity with modern MLOps practices and tools (MLflow, model registries, feature stores).

  • Experience designing semantic layers using tooling such as dbt Semantic Layer, Cube, or equivalent.

  • Ad server or retail media technology data modeling experience.

  • Experience with data mesh or data product design patterns at scale.

  • Event-driven architecture experience.

  • Familiarity with advanced Databricks optimization patterns, including liquid clustering and bitmap indexing for high-performance analytical workloads.

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